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The basic process of federated learning involves a central server distributing a global model, local nodes training on local data and uploading only gradients or model weight differences, and the server aggregating the updates via algorithms such as FedAvg before distributing a new global model.
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10/9/2026
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UnverifiedIn the FedAvg (Federated Averaging) algorithm, the server distributes the global model to each client, each party completes several rounds of gradient updates locally and then uploads model weights or gradients, and the server generates a new global model by taking a weighted average according to sample size.71% similarUnverified在联邦学习框架中，各参与方的模型梯度可通过加性秘密共享进行聚合，使协调服务器仅能获取加总后梯度而无法推断单一参与方原始数据69% similarUnverifiedTraining large models requires understanding the AllReduce communication primitive in NCCL (NVIDIA Collective Communications Library), which is essentially a process synchronization problem in distributed systems69% similarVerifiedLIME通过在目标样本周围构造扰动数据集，训练局部可解释的线性模型来近似复杂模型的局部行为66% similar
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