Chen, M., He, T., Lao, Q., Liu, Q., Ong, Y.-S., Tang, X., Wu, X., & Yu, H. (2024). Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning. Advances in Neural Information Processing Systems 37, 54974–55004. doi: 10.52202/079017-1744
Abstract:
Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that engage in business activities are key sources of FL-PTs. The resulting FL ecosystem has two features: (i) self-interest, and (ii) competition among FL-PTs. This requires the desirable FL-PT selection strategy to simultaneously mitigate the problems of free riders and conflicts of interest among competitors. To this end, we propose an optimal FL collaboration formation strategy -FedEgoists- which ensures that: (1) a FL-PT can benefit from FL if and only if it benefits the FL ecosystem, and (2) a FL-PT will not contribute to its competitors or their supporters. It provides an efficient clustering solution to group FL-PTs into coalitions, ensuring that within each coalition, FL-PTs share the same interest. We theoretically prove that the FL-PT coalitions formed are optimal since no coalitions can collaborate together to improve the utility of any of their members. Extensive experiments on widely adopted benchmark datasets demonstrate the effectiveness of FedEgoists compared to nine state-of-the-art baseline methods, and its ability to establish efficient collaborative networks in cross-silos FL with FL-PTs that engage in business activities.
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Funding Info:
This work is supported in part by the National Key R&D Program of China under Grant
2024YFE0200500. This research is supported in part by the National Natural Science Foundation of
China under 62327801. This research is supported, in part, by the National Research Foundation
Singapore and DSO National Laboratories under the AI Singapore Programme (No: AISG2-RP-2020-
019); and by A*STAR under the RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund (No.
A20G8b0102), Singapore. This research/project is supported by the National Research Foundation,
Singapore and Infocomm Media Development Authority under its Trust Tech Funding Initiative. Any
opinions, findings and conclusions or recommendations expressed in this material are those of the
author(s) and do not reflect the views of National Research Foundation, Singapore and Infocomm
Media Development Authority.