Reputation-Aware Federated Learning Client Selection Based on Stochastic Integer Programming

doi:10.1109/TBDATA.2022.3191332

Reputation-Aware Federated Learning Client Selection Based on Stochastic Integer Programming

2024-12-01

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AI

DOI：10.1109/TBDATA.2022.3191332

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Federated Learning(FL) has attracted wide research interest due to its potential in building machine learning models while preserving users' data privacy. However, due to the distributive nature of FL, it is vulnerable to misbehavior from participating worker nodes. Thus, it is important to select clients to participate in FL. Recent studies on FL client selection focus on the perspective of improving model training efficiency and performance, without holistically considering potential misbehavior and the cost of hiring. To bridge this gap, we propose a first-of-its-kind reputation-aware Stochastic integer programming-based FL Client Selection method (SCS). It can optimally select and compensate clients with different reputation profiles. Extensive experiments show that SCS achieves the most advantageous performance-cost trade-off compared to other existing state-of-the-art approaches.

Keywords:

Biological system modeling

Training

Computational modeling

Uncertainty

Costs

Stochastic processes

Data models

Federated learning

reputation

client selection

stochastic integer programming

Reputation-Aware Federated Learning Client Selection Based on Stochastic Integer Programming

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