GatedAFL: Privacy-Preserving and Resource-Efficient Asynchronous Personalized Federated Learning with Trainable Thresholds

Conventional federated learning (FL) methods typically rely on exchanging model parameters or loss gradients between clients and the server, which incurs substantial communication overhead and exposes sensitive information embedded in model updates. Threshold-gated training offers a more compact alternative, in which a learnable threshold per neuron determines whether its associated parameters are retained or pruned, and only these thresholds need to be communicated. This paper proposes GatedAFL, a threshold-gating method for asynchronous personalized FL. Clients keep their personalized parameters strictly local and upload only the threshold increment accumulated over their local updates. We first prove mathematically that threshold sharing never leaks more information than gradient sharing, and then quantify the privacy gap between the two approaches. Extensive experiments confirm that GatedAFL achieves enhanced resilience against privacy attacks while maintaining competitive accuracy. It reduces communication overhead by two to three orders of magnitude compared to FedAsync, and lowers computational cost by $55.6$ relative to SpaFL.