FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning

• URL: http://arxiv.org/abs/2412.14226v2
• Date: Sun, 29 Dec 2024 19:15:59 GMT
• Title: FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning
• Authors: Jordan Slessor, Dezheng Kong, Xiaofen Tang, Zheng En Than, Linglong Kong,
• Abstract summary: Federated learning (FL) involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. We propose textitFedSTaS, a client and data-level sampling method inspired by textitFedSTS and textitFedSampling. Experiments show that textitFedSTaS can achieve higher accuracy scores than those of textitFedSTS within a fixed number of training rounds.
• Score: 4.352691575442572
• License:
• Abstract: Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-preserving manner. In this paper, we propose \textit{FedSTaS}, a client and data-level sampling method inspired by \textit{FedSTS} and \textit{FedSampling}. In each federated learning round, \textit{FedSTaS} stratifies clients based on their compressed gradients, re-allocate the number of clients to sample using an optimal Neyman allocation, and sample local data from each participating clients using a data uniform sampling strategy. Experiments on three datasets show that \textit{FedSTaS} can achieve higher accuracy scores than those of \textit{FedSTS} within a fixed number of training rounds.

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