forked from mindspore-Ecosystem/mindspore
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云云联邦学习
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================================
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.. py:class:: mindspore.FederatedLearningManager(model, sync_frequency, sync_type="fixed", **kwargs)
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在训练过程中管理联邦学习。
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**参数：**
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- **model** (nn.Cell) - 一个用于联邦训练的模型。
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- **sync_frequency** (int) - 联邦学习中的参数同步频率。
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需要注意在数据下沉模式中，频率的单位是epoch的数量。否则，频率的单位是step的数量。
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在自适应同步频率模式下为初始同步频率，在固定频率模式下为同步频率。
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- **sync_type** (str) - 采用同步策略类型的参数。
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支持["fixed", "adaptive"]。默认值："fixed"。
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- fixed：参数的同步频率是固定的。
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- adaptive：参数的同步频率是自适应变化的。
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- **min_consistent_rate** (float) - 最小一致性比率阈值，该值越大同步频率提升难度越大。
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取值范围：大于等于0.0。默认值：1.1。
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- **min_consistent_rate_at_round** (int) - 最小一致性比率阈值的轮数，该值越大同步频率提升难度越大。
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取值范围：大于等于0。默认值：0。
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- **ema_alpha** (float) - 梯度一致性平滑系数，该值越小越会根据当前轮次的梯度分叉情况来判断频率是否
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需要改变，反之则会更加根据历史梯度分叉情况来判断。
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取值范围：(0.0, 1.0)。默认值：0.5。
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- **observation_window_size** (int) - 观察时间窗的轮数，该值越大同步频率减小难度越大。
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取值范围：大于0。默认值：5。
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- **frequency_increase_ratio** (int) - 频率提升幅度，该值越大频率提升幅度越大。
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取值范围：大于0。默认值：2。
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- **unchanged_round** (int) - 频率不发生变化的轮数，在前unchanged_round个轮次，频率不会发生变化。
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取值范围：大于等于0。默认值：0。
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.. note::
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这是一个实验原型，可能会有变化。
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.. py:method:: step_end(run_context)
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在step结束时同步参数。如果 `sync_type` 是"adaptive"，同步频率会在这里自适应的调整。
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**参数：**
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- **run_context** (RunContext) - 包含模型的相关信息。
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Federated-Server
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======================
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.. py:function:: mindspore.set_fl_context(**kwargs)
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设置联邦学习训练模式的context。
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.. note::
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设置属性时，必须输入属性名称。
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某些配置需要在特定角色上设置，有关详细信息，请参见下表：
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+-------------------------+---------------------------------+-----------------------------+
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| 功能分类 | 配置参数 | 联邦学习角色 |
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+=========================+=================================+=============================+
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| 组网配置 | enable_fl | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | server_mode | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | ms_role | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | worker_num | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | server_num | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | scheduler_ip | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | scheduler_port | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | fl_server_port | scheduler/server |
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| +---------------------------------+-----------------------------+
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| | scheduler_manage_port | scheduler |
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+-------------------------+---------------------------------+-----------------------------+
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| 训练配置 | start_fl_job_threshold | server |
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| +---------------------------------+-----------------------------+
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| | start_fl_job_time_window | server |
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| +---------------------------------+-----------------------------+
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| | update_model_ratio | server |
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| +---------------------------------+-----------------------------+
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| | update_model_time_window | server |
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| +---------------------------------+-----------------------------+
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| | fl_iteration_num | server |
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| +---------------------------------+-----------------------------+
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| | client_epoch_num | server |
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| +---------------------------------+-----------------------------+
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| | client_batch_size | server |
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| +---------------------------------+-----------------------------+
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| | client_learning_rate | server |
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| +---------------------------------+-----------------------------+
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| | worker_step_num_per_iteration | worker |
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| +---------------------------------+-----------------------------+
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| | global_iteration_time_window | server |
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+-------------------------+---------------------------------+-----------------------------+
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| 加密配置 | encrypt_type | server |
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| +---------------------------------+-----------------------------+
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| | share_secrets_ratio | server |
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| +---------------------------------+-----------------------------+
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| | cipher_time_window | server |
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| +---------------------------------+-----------------------------+
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| | reconstruct_secrets_threshold | server |
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| +---------------------------------+-----------------------------+
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| | dp_eps | server |
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| +---------------------------------+-----------------------------+
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| | dp_delta | server |
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| +---------------------------------+-----------------------------+
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| | dp_norm_clip | server |
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| +---------------------------------+-----------------------------+
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| | sign_k | server |
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| +---------------------------------+-----------------------------+
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| | sign_eps | server |
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| +---------------------------------+-----------------------------+
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| | sign_thr_ratio | server |
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| +---------------------------------+-----------------------------+
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| | sign_global_lr | server |
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| +---------------------------------+-----------------------------+
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| | sign_dim_out | server |
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+-------------------------+---------------------------------+-----------------------------+
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| 认证配置 | enable_ssl | server |
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| +---------------------------------+-----------------------------+
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| | client_password | server |
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| +---------------------------------+-----------------------------+
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| | server_password | server |
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| +---------------------------------+-----------------------------+
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| | root_first_ca_path | server |
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| +---------------------------------+-----------------------------+
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| | pki_verify | server |
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| +---------------------------------+-----------------------------+
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| | root_second_ca_path | server |
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| +---------------------------------+-----------------------------+
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| | equip_crl_path | server |
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| +---------------------------------+-----------------------------+
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| | replay_attack_time_diff | server |
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| +---------------------------------+-----------------------------+
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| | http_url_prefix | server |
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+-------------------------+---------------------------------+-----------------------------+
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| 容灾和运维配置 | fl_name | server |
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| +---------------------------------+-----------------------------+
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| | config_file_path | scheduler/server |
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| +---------------------------------+-----------------------------+
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| | checkpoint_dir | server |
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+-------------------------+---------------------------------+-----------------------------+
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| 压缩配置 | upload_compress_type | server |
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| +---------------------------------+-----------------------------+
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| | upload_sparse_rate | server |
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| +---------------------------------+-----------------------------+
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| | download_compress_type | server |
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+-------------------------+---------------------------------+-----------------------------+
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**参数：**
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- **enable_fl** (bool) - 是否启用联邦学习训练模式。默认值：False。
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- **server_mode** (str) - 描述服务器模式，它必须是'FEDERATED_LEARNING'和'HYBRID_TRAINING'中的一个。
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- **ms_role** (str) - 进程在联邦学习模式中的角色，
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它必须是'MS_SERVER'、'MS_WORKER'和'MS_SCHED'中的一个。
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- **worker_num** (int) - 云侧训练进程的数量。
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- **server_num** (int) - 联邦学习服务器的数量。默认值：0。
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- **scheduler_ip** (str) - 调度器IP。默认值：'0.0.0.0'。
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- **scheduler_port** (int) - 调度器端口。默认值：6667。
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- **fl_server_port** (int) - 服务器端口。默认值：6668。
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- **start_fl_job_threshold** (int) - 开启联邦学习作业的阈值计数。默认值：1。
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- **start_fl_job_time_window** (int) - 开启联邦学习作业的时间窗口持续时间，以毫秒为单位。默认值：300000。
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- **update_model_ratio** (float) - 计算更新模型阈值计数的比率。默认值：1.0。
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- **update_model_time_window** (int) - 更新模型的时间窗口持续时间，以毫秒为单位。默认值：300000。
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- **fl_name** (str) - 联邦学习作业名称。默认值：""。
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- **fl_iteration_num** (int) - 联邦学习的迭代次数，即客户端和服务器的交互次数。默认值：20。
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- **client_epoch_num** (int) - 客户端训练epoch数量。默认值：25。
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- **client_batch_size** (int) - 客户端训练数据batch数。默认值：32。
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- **client_learning_rate** (float) - 客户端训练学习率。默认值：0.001。
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- **worker_step_num_per_iteration** (int) - 端云联邦中，云侧训练进程在与服务器通信之前的独立训练步数。默认值：65。
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- **encrypt_type** (str) - 用于联邦学习的安全策略，可以是'NOT_ENCRYPT'、'DP_ENCRYPT'、
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'PW_ENCRYPT'、'STABLE_PW_ENCRYPT'或'SIGNDS'。如果是'DP_ENCRYPT'，则将对客户端应用差分隐私模式，
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隐私保护效果将由上面所述的dp_eps、dp_delta、dp_norm_clip确定。如果'PW_ENCRYPT'，则将应用成对（pairwise，PW）安全聚合
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来保护客户端模型在跨设备场景中不被窃取。如果'STABLE_PW_ENCRYPT'，则将应用成对安全聚合来保护客户端模型在云云联邦场景中
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免受窃取。如果'SIGNDS'，则将在于客户端上使用SignDS策略。SignDS的介绍可以参照：
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`SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection <https://dl.acm.org/doi/abs/10.1145/3517820>`_。
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默认值：'NOT_ENCRYPT'。
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- **share_secrets_ratio** (float) - PW：参与秘密分享的客户端比例。默认值：1.0。
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- **cipher_time_window** (int) - PW：每个加密轮次的时间窗口持续时间，以毫秒为单位。默认值：300000。
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- **reconstruct_secrets_threshold** (int) - PW：秘密重建的阈值。默认值：2000。
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- **dp_eps** (float) - DP：差分隐私机制的epsilon预算。dp_eps越小，隐私保护效果越好。默认值：50.0。
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- **dp_delta** (float) - DP：差分隐私机制的delta预算，通常等于客户端数量的倒数。dp_delta越小，隐私保护效果越好。默认值：0.01。
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- **dp_norm_clip** (float) - DP：差分隐私梯度裁剪的控制因子。建议其值为0.5~2。默认值：1.0。
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- **sign_k** (float) - SignDS：Top-k比率，即Top-k维度的数量除以维度总数。建议取值范围在(0, 0.25]内。默认值：0.01。
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- **sign_eps** (float) - SignDS：隐私预算。该值越小隐私保护力度越大，精度越低。建议取值范围在(0, 100]内。默认值：100。
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- **sign_thr_ratio** (float) - SignDS：预期Top-k维度的阈值。建议取值范围在[0.5, 1]内。默认值：0.6。
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- **sign_global_lr** (float) - SignDS：分配给选定维的常量值。适度增大该值会提高收敛速度，但有可能让模型梯度爆炸。取值必须大于0。默认值：1。
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- **sign_dim_out** (int) - SignDS：输出维度的数量。建议取值范围在[0, 50]内。默认值：0。
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- **config_file_path** (str) - 用于集群容灾恢复的配置文件路径、认证相关参数以及文件路径、评价指标文件路径和运维相关文件路径。默认值：""。
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- **scheduler_manage_port** (int) - 用于扩容/缩容的调度器管理端口。默认值：11202。
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- **enable_ssl** (bool) - 设置联邦学习开启SSL安全通信。默认值：False。
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- **client_password** (str) - 解密客户端证书中存储的秘钥的密码。默认值：""。
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- **server_password** (str) - 解密服务器证书中存储的秘钥的密码。默认值：""。
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- **pki_verify** (bool) - 如果为True，则将打开服务器和客户端之间的身份验证。
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您还应从https://pki.consumer.huawei.com/ca/下载Root CA证书、Root CA G2证书和移动设备CRL证书。
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需要注意的是，只有当客户端是具有HUKS服务的Android环境时，pki_verify可以为True。默认值：False。
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- **root_first_ca_path** (str) - Root CA证书的文件路径。当pki_verify为True时，需要设置该值。默认值：""。
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- **root_second_ca_path** (str) - Root CA G2证书的文件路径。当pki_verify为True时，需要设置该值。默认值：""。
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- **equip_crl_path** (str) - 移动设备CRL证书的文件路径。当pki_verify为True时，需要设置该值。默认值：""。
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- **replay_attack_time_diff** (int) - 证书时间戳验证的最大可容忍错误（毫秒）。默认值：600000。
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- **http_url_prefix** (str) - 设置联邦学习端云通信的http路径。默认值：""。
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- **global_iteration_time_window** (int) - 一次迭代的全局时间窗口，轮次（ms）。默认值：3600000。
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- **checkpoint_dir** (str) - server读取和保存模型文件的目录。若没有设置则不读取和保存模型文件。默认值：""。
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- **upload_compress_type** (str) - 上传压缩方法。可以是'NO_COMPRESS'或'DIFF_SPARSE_QUANT'。如果是'NO_COMPRESS'，则不对上传的模型
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进行压缩。如果是'DIFF_SPARSE_QUANT'，则对上传的模型使用权重差+稀疏+量化压缩策略。默认值：'NO_COMPRESS'。
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- **upload_sparse_rate** (float) - 上传压缩稀疏率。稀疏率越大，则压缩率越小。取值范围：(0, 1.0]。默认值：0.4。
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- **download_compress_type** (str) - 下载压缩方法。可以是'NO_COMPRESS'或'QUANT'。如果是'NO_COMPRESS'，则不对下载的模型进行压缩。
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如果是'QUANT'，则对下载的模型使用量化压缩策略。默认值：'NO_COMPRESS'。
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**异常：**
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**ValueError** - 如果输入key不是联邦学习模式context中的属性。
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.. py:function:: mindspore.get_fl_context(attr_key)
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根据key获取联邦学习模式context中的属性值。
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**参数：**
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- **attr_key** (str) - 属性的key。
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请参考 `set_fl_context` 中的参数来决定应传递的key。
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**返回：**
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根据key返回属性值。
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**异常：**
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**ValueError** - 如果输入key不是联邦学习模式context中的属性。
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@ -783,7 +783,7 @@ def set_context(**kwargs):
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If enable_graph_kernel is set to True, acceleration can be enabled.
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For details of graph kernel fusion, please check
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`Enabling Graph Kernel Fusion
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<https://www.mindspore.cn/docs/en/master/design/graph_fusion_engine.html>`_.
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<https://www.mindspore.cn/tutorials/experts/en/master/debug/graph_fusion_engine.html>`_.
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graph_kernel_flags (str) –
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Optimization options of graph kernel fusion, and the priority is higher when it conflicts
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with enable_graph_kernel. Only for experienced users.
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@ -1050,29 +1050,126 @@ def set_fl_context(**kwargs):
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"""
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Set federated learning training mode context.
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Note:
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Attribute name is required for setting attributes.
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Some configurations need to be set on specific roles. For details, see the following table:
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+-------------------------+---------------------------------+-----------------------------+
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| Function Classification | Configuration Parameters | Federated Role |
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+=========================+=================================+=============================+
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| Network Configuration | enable_fl | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | server_mode | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | ms_role | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | worker_num | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | server_num | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | scheduler_ip | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | scheduler_port | scheduler/server/worker |
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| +---------------------------------+-----------------------------+
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| | fl_server_port | scheduler/server |
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| +---------------------------------+-----------------------------+
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| | scheduler_manage_port | scheduler |
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+-------------------------+---------------------------------+-----------------------------+
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| Training Configuration | start_fl_job_threshold | server |
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| +---------------------------------+-----------------------------+
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| | start_fl_job_time_window | server |
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| +---------------------------------+-----------------------------+
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| | update_model_ratio | server |
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| +---------------------------------+-----------------------------+
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| | update_model_time_window | server |
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| +---------------------------------+-----------------------------+
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| | fl_iteration_num | server |
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| +---------------------------------+-----------------------------+
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| | client_epoch_num | server |
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| +---------------------------------+-----------------------------+
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| | client_batch_size | server |
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| +---------------------------------+-----------------------------+
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| | client_learning_rate | server |
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| +---------------------------------+-----------------------------+
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| | worker_step_num_per_iteration | worker |
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| +---------------------------------+-----------------------------+
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| | global_iteration_time_window | server |
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+-------------------------+---------------------------------+-----------------------------+
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| Encrypt Configuration | encrypt_type | server |
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| +---------------------------------+-----------------------------+
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| | share_secrets_ratio | server |
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| +---------------------------------+-----------------------------+
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| | cipher_time_window | server |
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||||
| +---------------------------------+-----------------------------+
|
||||
| | reconstruct_secrets_threshold | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | dp_eps | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | dp_delta | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | dp_norm_clip | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | sign_k | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | sign_eps | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | sign_thr_ratio | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | sign_global_lr | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | sign_dim_out | server |
|
||||
+-------------------------+---------------------------------+-----------------------------+
|
||||
| Cert Configuration | enable_ssl | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | client_password | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | server_password | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | root_first_ca_path | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | pki_verify | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | root_second_ca_path | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | equip_crl_path | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | replay_attack_time_diff | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | http_url_prefix | server |
|
||||
+-------------------------+---------------------------------+-----------------------------+
|
||||
| DR and OM Configuration | fl_name | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | config_file_path | scheduler/server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | checkpoint_dir | server |
|
||||
+-------------------------+---------------------------------+-----------------------------+
|
||||
| Compress Configuration | upload_compress_type | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | upload_sparse_rate | server |
|
||||
| +---------------------------------+-----------------------------+
|
||||
| | download_compress_type | server |
|
||||
+-------------------------+---------------------------------+-----------------------------+
|
||||
|
||||
Args:
|
||||
enable_fl (bool): Whether to enable federated learning training mode.
|
||||
Default: False.
|
||||
server_mode (str): Describe the server mode, which must one of 'FEDERATED_LEARNING' and 'HYBRID_TRAINING'.
|
||||
Default: 'FEDERATED_LEARNING'.
|
||||
ms_role (str): The process's role in the federated learning mode,
|
||||
which must be one of 'MS_SERVER', 'MS_WORKER' and 'MS_SCHED'.
|
||||
Default: 'MS_SERVER'.
|
||||
worker_num (int): The number of workers. For current version, this must be set to 1 or 0.
|
||||
worker_num (int): The number of workers.
|
||||
server_num (int): The number of federated learning servers. Default: 0.
|
||||
scheduler_ip (str): The scheduler IP. Default: '0.0.0.0'.
|
||||
scheduler_port (int): The scheduler port. Default: 6667.
|
||||
fl_server_port (int): The http port of the federated learning server.
|
||||
Normally for each server this should be set to the same value. Default: 6668.
|
||||
enable_fl_client (bool): Whether this process is federated learning client. Default: False.
|
||||
fl_server_port (int): The server port. Default: 6668.
|
||||
start_fl_job_threshold (int): The threshold count of startFLJob. Default: 1.
|
||||
start_fl_job_time_window (int): The time window duration for startFLJob in millisecond. Default: 3000.
|
||||
start_fl_job_time_window (int): The time window duration for startFLJob in millisecond. Default: 300000.
|
||||
share_secrets_ratio (float): The ratio for computing the threshold count of share secrets. Default: 1.0.
|
||||
update_model_ratio (float): The ratio for computing the threshold count of updateModel. Default: 1.0.
|
||||
cipher_time_window (int): The time window duration for each cipher round in millisecond. Default: 300000.
|
||||
reconstruct_secrets_threshold (int): The threshold count of reconstruct threshold. Default: 0.
|
||||
update_model_time_window (int): The time window duration for updateModel in millisecond. Default: 3000.
|
||||
fl_name (string): The federated learning job name. Default: ''.
|
||||
reconstruct_secrets_threshold (int): The threshold count of reconstruct threshold. Default: 2000.
|
||||
update_model_time_window (int): The time window duration for updateModel in millisecond. Default: 300000.
|
||||
fl_name (str): The federated learning job name. Default: ''.
|
||||
fl_iteration_num (int): Iteration number of federated learning,
|
||||
which is the number of interactions between client and server. Default: 20.
|
||||
client_epoch_num (int): Client training epoch number. Default: 25.
|
||||
|
|
@ -1086,7 +1183,7 @@ def set_fl_context(**kwargs):
|
|||
client number. The smaller the dp_delta, the better the privacy protection effect. Default: 0.01.
|
||||
dp_norm_clip (float): A factor used for clipping model's weights for differential mechanism. Its value is
|
||||
suggested to be 0.5~2. Default: 1.0.
|
||||
encrypt_type (string): Secure schema for federated learning, which can be 'NOT_ENCRYPT', 'DP_ENCRYPT',
|
||||
encrypt_type (str): Secure schema for federated learning, which can be 'NOT_ENCRYPT', 'DP_ENCRYPT',
|
||||
'PW_ENCRYPT', 'STABLE_PW_ENCRYPT' or 'SIGNDS'. If 'DP_ENCRYPT', differential privacy schema would be applied
|
||||
for clients and the privacy protection effect would be determined by dp_eps, dp_delta and dp_norm_clip
|
||||
as described above. If 'PW_ENCRYPT', pairwise secure aggregation would be applied to protect clients'
|
||||
|
|
@ -1099,9 +1196,10 @@ def set_fl_context(**kwargs):
|
|||
sign_thr_ratio (float): SignDS: Threshold of the expected topk dimension. Default: 0.6.
|
||||
sign_global_lr (float): SignDS: The constant value assigned to the selected dimension. Default: 1.
|
||||
sign_dim_out (int): SignDS: Number of output dimensions. Default: 0.
|
||||
config_file_path (string): Configuration file path used by recovery. Default: ''.
|
||||
config_file_path (str): Configuration file paths used for cluster disaster recovery, cert-related parameters
|
||||
and file paths, metrics-related file path and O&M-related file paths. Default: ''.
|
||||
scheduler_manage_port (int): scheduler manage port used to scale out/in. Default: 11202.
|
||||
enable_ssl (bool): Set PS SSL mode enabled or disabled. Default: False.
|
||||
enable_ssl (bool): Enable SSL secure communication for federated learning. Default: False.
|
||||
client_password (str): Password to decrypt the secret key stored in the client certificate. Default: ''.
|
||||
server_password (str): Password to decrypt the secret key stored in the server certificate. Default: ''.
|
||||
pki_verify (bool): If True, the identity verification between server and clients would be turned on.
|
||||
|
|
@ -1116,12 +1214,21 @@ def set_fl_context(**kwargs):
|
|||
pki_verify is True. Default: "".
|
||||
replay_attack_time_diff (int): The maximum tolerable error of certificate timestamp verification (ms).
|
||||
Default: 600000.
|
||||
http_url_prefix (string): The http url prefix for http server.
|
||||
http_url_prefix (str): Setting the http path for federated learning cross-device communication.
|
||||
Default: "".
|
||||
global_iteration_time_window (unsigned long): The global iteration time window for one iteration
|
||||
global_iteration_time_window (int): The global iteration time window for one iteration
|
||||
with rounds(ms). Default: 3600000.
|
||||
checkpoint_dir (string): The Server model checkpoint directory. If no checkpoint dir is set,
|
||||
the startup script directory is used by default. Default: "".
|
||||
checkpoint_dir (str): The directory where the server reads and saves model files. If it is not set,
|
||||
the model file will not be read and saved. Default: "".
|
||||
upload_compress_type (str): Upload compression method, which can be 'NO_COMPRESS' or 'DIFF_SPARSE_QUANT'.
|
||||
If 'NO_COMPRESS', our framework does not compress the uploaded model. If 'DIFF_SPARSE_QUANT', the
|
||||
weight difference + sparse + quantization compression strategy is used for the uploaded model.
|
||||
Default: 'NO_COMPRESS'.
|
||||
upload_sparse_rate (float): Upload compression sparsity ratio. The larger the sparsity ratio, the smaller the
|
||||
compression ratio. Value range: (0, 1.0]. Default: 0.4.
|
||||
download_compress_type (str): Download the compression method, which can be 'NO_COMPRESS' or 'QUANT'. If
|
||||
'NO_COMPRESS', our framework does not compress the downloaded model. If'QUANT', the quantized compression
|
||||
strategy is used for the downloaded model. Default: 'NO_COMPRESS'.
|
||||
Raises:
|
||||
ValueError: If input key is not the attribute in federated learning mode context.
|
||||
|
||||
|
|
|
|||
|
|
@ -94,6 +94,27 @@ class FederatedLearningManager(Callback):
|
|||
|
||||
- fixed: The frequency of parameter synchronization is fixed.
|
||||
- adaptive: The frequency of parameter synchronization changes adaptively.
|
||||
min_consistent_rate (float): Minimum consistency ratio threshold. The greater the value, the more
|
||||
difficult it is to improve the synchronization frequency.
|
||||
Value range: greater than or equal to 0.0. Default: 1.1.
|
||||
min_consistent_rate_at_round (int): The number of rounds of the minimum consistency ratio threshold.
|
||||
The greater the value, the more difficult it is to improve the
|
||||
synchronization frequency.
|
||||
Value range: greater than or equal to 0. Default: 0.
|
||||
ema_alpha (float): Gradient consistency smoothing coefficient. The smaller the value, the more the
|
||||
frequency will be judged according to the gradient bifurcation of the current round
|
||||
more. Otherwise it will be judged according to the historical gradient bifurcation
|
||||
more.
|
||||
Value range: (0.0, 1.0). Default: 0.5.
|
||||
observation_window_size (int): The number of rounds in the observation time window. The greater the
|
||||
value, the more difficult it is to reduce the synchronization frequency.
|
||||
Value range: greater than 0. Default: 5.
|
||||
frequency_increase_ratio (int): Frequency increase amplitude. The greater the value, the greater the
|
||||
frequency increase amplitude.
|
||||
Value range: greater than 0. Default: 2.
|
||||
unchanged_round (int): The number of rounds whose frequency does not change. The frequency is unchanged
|
||||
before unchanged_round rounds.
|
||||
Value range: greater than or equal to 0. Default: 0.
|
||||
|
||||
Note:
|
||||
This is an experimental prototype that is subject to change.
|
||||
|
|
@ -182,7 +203,10 @@ class FederatedLearningManager(Callback):
|
|||
abs_grads[self._as_prefix + param.name] = np.abs(param.asnumpy() - self._last_param[param.name])
|
||||
for param in self._model.trainable_params():
|
||||
if self._as_prefix in param.name:
|
||||
param.set_data(Parameter(abs_grads[param.name]))
|
||||
try:
|
||||
param.set_data(Parameter(abs_grads[param.name]))
|
||||
except KeyError:
|
||||
print("{} is not in abs_grads".format(param.name))
|
||||
|
||||
def _as_analyze_gradient(self):
|
||||
"""
|
||||
|
|
|
|||
Loading…
Reference in New Issue