Reliability-Aware Client Selection in Asynchronous and Heterogeneous Federated Learning

Abstract

Polaris is a client selection framework for asynchronous and heterogeneous federated learning that employs quality-aware sampling to accelerate convergence. Its reliance on scalar update norms as quality indicators exposes it to exploitation, in which clients submit near-zero Gaussian updates that inflate the update magnitude proxy within the Polaris sampling objective without contributing meaningful gradient information. This work introduces a lightweight reliability-aware extension that embeds two server-side signals, Exponential Moving Average (EMA)-based magnitude credibility and cosine similarity-based directional alignment, directly into the staleness and aggregation weight proxy computation without modifying the geometric programming objective or aggregation semantics. Experiments across ResNet-18 on CIFAR-10, LeNet-5 on FEMNIST, and LeNet-5 on MNIST under non-IID data distributions confirm that attack effectiveness and defense discrimination are governed by task complexity and non-IID (non-Independent and Identically Distributed) degree. The proposed method demonstrates partial resistance to convergence collapse at lower adversarial ratios while introducing only a marginal accuracy overhead under clean conditions.