Accelerating Federated Learning via Sequential Training of Grouped Heterogeneous Clients

Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds required to reach centralized performance. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, i.e., superclients, to learn more robust models before the server-side averaging step. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Our method can be easily integrated with other approaches available in the literature, and empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We evaluate our method across multiple FL benchmarks, establishing its effectiveness in both i.i.d. and non-i.i.d. scenarios. Lastly, we highlight that the sequential training introduced here does not introduce additional privacy concerns when compared to the de facto standard, FedAvg.