Personalized federated learning with fairness, robustness, and collaboration incentives
Authors:
- Fahad SabahFSFahad Sabah
- Yuwen ChenYCYuwen Chen
- Zhen YangZYZhen Yang
- Muhammad AzamMAMuhammad Azam
- Nadeem AhmadNANadeem Ahmad
- Raheem SarwarRSRaheem Sarwar
- 26 July 2025
- journal article
- Published by Elsevier in Applied Soft Computing
- Vol. 183
Abstract
No abstract availableFunding Information
- Beijing Municipal Commission of Education (KM202210005028)
- National Key Research and Development Program of China
- Beijing Municipal Education Commission
- National Natural Science Foundation of China (92167102, 62302020)
- Key Technologies Research and Development Program (2022YFB3103100)
This publication has 82 references indexed in Scilit:
- Auction-based client selection for online Federated LearningInformation Fusion, 2024
- TiFLCS-MARP: Client selection and model pricing for federated learning in data marketsExpert Systems with Applications, 2024
- Federated learning based on Stackelberg game in unmanned-aerial-vehicle-enabled mobile edge computingExpert Systems with Applications, 2023
- FedMBC: Personalized federated learning via mutually beneficial collaborationComputer Communications, 2023
- A reliable and fair federated learning mechanism for mobile edge computingComputer Networks, 2023
- FedDCS: A distributed client selection framework for cross device federated learningFuture Generation Computer Systems, 2023
- Personalized federated learning with model interpolation among client clusters and its application in smart homeWorld Wide Web, 2023
- Trust-driven reinforcement selection strategy for federated learning on IoT devicesComputing, 2022
- Formal verification of group and propagated trust in multi-agent systemsAutonomous Agents and Multi-Agent Systems, 2022
- Collaborative Fairness in Federated Learning2020