[Paper Review] Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

Dan Ben Ami, Kobi Cohen+1 more|arXiv (Cornell University)|Mar 18, 2023

Advanced Bandit Algorithms ResearchDecision Sciences8 citations

TL;DR

The paper proposes a Bandit Scheduling for Federated Learning (BSFL) algorithm to select clients in FL to minimize training latency while preserving generalization, with theoretical regret guarantees and empirical validation.

ABSTRACT

Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy considerations, and communication resources. In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time. The server updates the global model and broadcasts it back to the clients. The client selection problem in FL is to schedule a subset of the clients for training and transmission at each given time so as to optimize the learning performance. In this paper, we present a novel multi-armed bandit (MAB)-based approach for client selection to minimize the training latency without harming the ability of the model to generalize, that is, to provide reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). We analyze BSFL theoretically, and show that it achieves a logarithmic regret, defined as the loss of BSFL as compared to a genie that has complete knowledge about the latency means of all clients. Furthermore, simulation results using synthetic and real datasets demonstrate that BSFL is superior to existing methods.

Motivation & Objective

• Motivate the need for efficient client selection in federated learning to balance latency and generalization.
• Propose a novel Bandit Scheduling for FL (BSFL) algorithm.
• Provide theoretical analysis showing logarithmic regret relative to an oracle with full latency knowledge.
• Demonstrate through simulations that BSFL outperforms existing methods on synthetic and real data.

Proposed method

• Formulate client scheduling as a multi-armed bandit problem.
• Develop the BSFL algorithm that selects and schedules clients to minimize latency while preserving generalization.
• Provide theoretical analysis proving logarithmic regret against a genie with knowledge of client latencies.
• Evaluate BSFL via simulations on synthetic and real datasets to compare with existing methods.

Experimental results

Research questions

• RQ1 How can client selection in federated learning be designed to minimize training latency without harming generalization?
• RQ2 What regret guarantees can be established for a bandit-based client selection strategy in FL?
• RQ3 How does BSFL perform compared to existing client selection methods on synthetic and real data?
• RQ4 Under what conditions does BSFL achieve near-optimal scheduling in practice?

Key findings

• BSFL achieves logarithmic regret with respect to an oracle that knows all clients’ latency means.
• BSFL demonstrates superior performance over existing methods in simulations on both synthetic and real datasets.
• The approach balances latency reduction with maintaining the model’s generalization capabilities for new observations.
• Theoretical analysis supports the efficiency and reliability of the proposed scheduling strategy.