Towards Asynchronous Client Collaboration in Personalized Federated Learning

Collaboration-based personalized federated learning (Co-PFL) achieves superior accuracy on non-IID data by leveraging a collaboration graph that captures pairwise similarities among client data distributions for personalized model aggregation. However, its synchronous operation suffers from substantial delays caused by slow clients, limiting its applicability in latency-sensitive IoT applications. A natural alternative is asynchronous Co-PFL, where the server processes model updates as they arrive. Yet, asynchrony introduces staleness that distorts both collaboration graph estimation and personalized model aggregation, two tightly coupled components in Co-PFL. Existing asynchronous federated learning methods only handle staleness in global model aggregation and fail to maintain this coupling. To bridge this gap, we propose PACE, the first asynchronous Co-PFL framework that jointly manages staleness across collaboration estimation and model aggregation. PACE employs two lightweight, theoretically grounded server-side mechanisms: a collaboration-aware buffer update that adaptively refreshes buffered models based on staleness and collaboration relevance, and a staleness-triggered model multicast that selectively disseminates fresh aggregates when collaboration estimation error exceeds a threshold. Extensive evaluations show that PACE matches the accuracy of state-of-the-art synchronous Co-PFL methods, while reducing convergence time by up to 8.58×, enabling practical Co-PFL with IoT devices.