# Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2012.03270 · HTML: https://mlsanity.com/paper/2012.03270/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback](https://mlsanity.com/paper/2112.14332/) (2021)
- [Efficient Node Selection in Private Personalized Decentralized Learning](https://mlsanity.com/paper/2301.12755/) (2023)
- [Differentially Private Federated Combinatorial Bandits with Constraints](https://mlsanity.com/paper/2206.13192/) (2022)
- [Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning](https://mlsanity.com/paper/2310.00198/) (2023)
- [Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance](https://mlsanity.com/paper/2310.02698/) (2023)
- [Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation](https://mlsanity.com/paper/2412.20253/) (2024)
- [FedEGG: Federated Learning with Explicit Global Guidance](https://mlsanity.com/paper/2404.11888/) (2024)

## Abstract (arXiv)

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.