Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
This work addresses the problems of high generalization error and slow convergence for federated learning practitioners by improving client sampling and model averaging.
This paper tackles the challenges of biased model averaging and lack of prior knowledge in client sampling within federated learning. The proposed FedCM algorithm significantly outperformed state-of-the-art algorithms, achieving up to 37.25% higher generalization accuracy and a 4.17 times faster convergence rate.
Federated learning has emerged as an innovative paradigm of collaborative machine learning. Unlike conventional machine learning, a global model is collaboratively learned while data remains distributed over a tremendous number of client devices, thus not compromising user privacy. However, several challenges still remain despite its glowing popularity; above all, the global aggregation in federated learning involves the challenge of biased model averaging and lack of prior knowledge in client sampling, which, in turn, leads to high generalization error and slow convergence rate, respectively. In this work, we propose a novel algorithm called FedCM that addresses the two challenges by utilizing prior knowledge with multi-armed bandit based client sampling and filtering biased models with combinatorial model averaging. Based on extensive evaluations using various algorithms and representative heterogeneous datasets, we showed that FedCM significantly outperformed the state-of-the-art algorithms by up to 37.25% and 4.17 times, respectively, in terms of generalization accuracy and convergence rate.