Bi-granularity Weighted Aggregation for Asynchronous Federated Learning

Asynchronous federated learning (AFL) enables collaborative model training under heterogeneous computation and communication latency, where participating clients may update and transmit their local models at substantially different speeds. Existing AFL methods typically discard updates from stragglers or reduce their aggregation weights according to update staleness. However, under severe system heterogeneity, such strategies may underutilise valuable client information, slow convergence, and increase discrepancies between the aggregated global model and the models that would have been obtained with more complete client participation. To address these challenges, we propose a novel bi-granularity weighted aggregation framework for AFL. The key idea is to reconstruct a pseudo local model for each straggling client, allowing federated aggregation to proceed by jointly incorporating received local models and reconstructed models for unavailable stragglers. To balance reconstruction quality and computational efficiency, rather than generating an entire model from scratch, our method reconstructs each pseudo model by reassembling neural blocks selected from available client models and leveraging the straggler's statistical fingerprints. During global aggregation, client contributions are jointly weighted at both the model level and neural-block level, enabling the server to account for heterogeneity at different granularities. Extensive experiments across diverse large-scale asynchronous settings demonstrate that the proposed method consistently improves performance over representative AFL baselines. Further analysis shows that the reconstructed pseudo models closely approximate the actual local models subsequently obtained from straggling clients, providing empirical evidence for the effectiveness of the proposed reconstruction mechanism.