Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning

no code implementations • 6 Feb 2024 • Meiying Zhang, Huan Zhao, Sheldon Ebron, Kan Yang

In this paper, we introduce a novel method called Fair, Robust, and Efficient Client Assessment (FRECA) for quantifying client contributions in FL.

Federated Learning

Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service

no code implementations • 5 Dec 2023 • Meiying Zhang, Huan Zhao, Sheldon Ebron, Ruitao Xie, Kan Yang

Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it.

Fairness Federated Learning +1