论文标题
基于强盗的沟通高效客户选择策略用于联合学习
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
论文作者
论文摘要
由于沟通限制和联合学习中的间歇性客户可用性，只有一部分客户才能参加每个培训回合。尽管大多数先前的工作都假设统一和公正的客户选择，但最新的偏见客户选择工作表明，选择较高本地损失的客户可以提高错误收敛速度。但是，先前提出的有偏见的选择策略要么需要额外的通信成本来评估确切的本地损失，要么利用陈旧的局部损失，甚至可以使模型差异。在本文中，我们提出了一种基于强盗的沟通式客户选择策略UCB-CS，该策略可以通过较低的通信开销来更快地收敛。我们还展示了如何使用客户选择来提高公平性。
Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works assume uniform and unbiased client selection, recent work on biased client selection has shown that selecting clients with higher local losses can improve error convergence speed. However, previously proposed biased selection strategies either require additional communication cost for evaluating the exact local loss or utilize stale local loss, which can even make the model diverge. In this paper, we present a bandit-based communication-efficient client selection strategy UCB-CS that achieves faster convergence with lower communication overhead. We also demonstrate how client selection can be used to improve fairness.
