The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative training of a machine learning model under the orchestration of a central server. In this setup, each client's data is private to itself and is not transferable to other clients or the server. Though FL paradigm has received significant interest recently from the research community, the problem of selecting the relevant clients w.r.t. the central server's learning objective is under-explored. We refer to these problems as Federated Relevant Client Selection (FRCS). Because the server doesn't have explicit control over the nature of data possessed by each client, the problem of selecting relevant clients is significantly complex in FL settings. In this paper, we resolve important and related FRCS problems viz., selecting clients with relevant data, detecting clients that possess data relevant to a particular target label, and rectifying corrupted data samples of individual clients. We follow a principled approach to address the above FRCS problems and develop a new federated learning method using the Shapley value concept from cooperative game theory. Towards this end, we propose a cooperative game involving the gradients shared by the clients. Using this game, we compute Shapley values of clients and then present Shapley value based Federated Averaging (S-FedAvg) algorithm that empowers the server to select relevant clients with high probability. S-FedAvg turns out to be critical in designing specific algorithms to address the FRCS problems. We finally conduct a thorough empirical analysis on image classification and speech recognition tasks to show the superior performance of S-FedAvg than the baselines in the context of supervised federated learning settings.
翻译:联邦学习模式(FL) 的范式与多个客户打交道, 在一个中央服务器的调控下, 参与机器学习模式的合作培训。 在这个设置中, 每个客户的数据是自己私有的, 无法转移到其他客户或服务器。 尽管FL的范式最近受到研究界的极大关注, 选择相关客户 w.r.t. 中央服务器的学习目标没有得到充分探讨。 我们称之为Freed 相关客户选择 。 由于服务器对每个客户拥有的数据的性质没有明确的控制, 选择相关客户的问题在 FL 设置中非常复杂。 在本文中, 我们解决了重要和相关的FRCS问题, 检测拥有特定目标标签相关数据客户的客户, 并纠正了个人客户的腐败数据样本。 我们遵循原则性的方法来解决上述问题, 并用合作性游戏理论的Spley 值概念开发新的Federferal化学习方法。 为此, 我们提议了一个合作性游戏的游戏规则化游戏规则, 使F的梯度与高级客户共享的直径(S- sliveralal) elview lavealal A) exal A exalal Axal acultcultislational lading a culting swequest sweal cultcultcultmalational