# MAB-Based Online Client Scheduling for Decentralized Federated Learning in the IoT

## TL;DR

This paper introduces an online client scheduling method for decentralized federated learning in IoT using multi-armed bandit algorithms to optimize performance and reduce delays.

## Contribution

A novel MAB-based online learning algorithm for client scheduling in DFL without prior client information.

## Key findings

- The proposed algorithm achieves asymptotic optimal performance in client scheduling.
- It significantly reduces the cumulative delay compared to existing methods.
- Theoretical analysis and experiments validate the algorithm's effectiveness.

## Abstract

Different from conventional federated learning (FL), which relies on a central server for model aggregation, decentralized FL (DFL) exchanges models among edge servers, thus improving the robustness and scalability. When deploying DFL into the Internet of Things (IoT), limited wireless resources cannot provide simultaneous access to massive devices. One must perform client scheduling to balance the convergence rate and model accuracy. However, the heterogeneity of computing and communication resources across client devices, combined with the time-varying nature of wireless channels, makes it challenging to estimate accurately the delay associated with client participation during the scheduling process. To address this issue, we investigate the client scheduling and resource optimization problem in DFL without prior client information. Specifically, the considered problem is reformulated as a multi-armed bandit (MAB) program, and an online learning algorithm that utilizes contextual multi-arm slot machines for client delay estimation and scheduling is proposed. Through theoretical analysis, this algorithm can achieve asymptotic optimal performance in theory. The experimental results show that the algorithm can make asymptotic optimal client selection decisions, and this method is superior to existing algorithms in reducing the cumulative delay of the system.