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Performance Evaluation Methodologies and Tools. 15th EAI International Conference, VALUETOOLS 2022, Virtual Event, November 2022, Proceedings

Research Article

Multi-Model Federated Learning with Provable Guarantees

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-31234-2_13,
        author={Neelkamal Bhuyan and Sharayu Moharir and Gauri Joshi},
        title={Multi-Model Federated Learning with Provable Guarantees},
        proceedings={Performance Evaluation Methodologies and Tools. 15th EAI International Conference, VALUETOOLS 2022, Virtual Event, November 2022, Proceedings},
        proceedings_a={VALUETOOLS},
        year={2023},
        month={5},
        keywords={Federated Learning Distributed Learning Optimization},
        doi={10.1007/978-3-031-31234-2_13}
    }
    
  • Neelkamal Bhuyan
    Sharayu Moharir
    Gauri Joshi
    Year: 2023
    Multi-Model Federated Learning with Provable Guarantees
    VALUETOOLS
    Springer
    DOI: 10.1007/978-3-031-31234-2_13
Neelkamal Bhuyan,*, Sharayu Moharir, Gauri Joshi1
  • 1: Carnegie Mellon University
*Contact email: neelkamalbhuyan@gmail.com

Abstract

Federated Learning (FL) is a variant of distributed learning where edge devices collaborate to learn a model without sharing their data with the central server or each other. We refer to the process of training multiple independent models simultaneously in a federated setting using a common pool of clients as multi-model FL. In this work, we propose two variants of the popular FedAvg algorithm for multi-model FL, with provable convergence guarantees. We further show that for the same amount of computation, multi-model FL can have better performance than training each model separately. We supplement our theoretical results with experiments in strongly convex, convex, and non-convex settings.

Keywords
Federated Learning Distributed Learning Optimization
Published
2023-05-03
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-31234-2_13
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