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FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients

Authors:

Yuanqing Zheng,

Jinsong HanAuthors Info & Claims

MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services

Pages 398 - 411

https://doi.org/10.1145/3643832.3661880

Published: 04 June 2024 Publication History

Abstract

Federated Learning (FL) facilitates collaborative training of a shared global model without exposing clients' private data. In practical FL systems, clients (e.g., edge servers, smartphones, and wearables) typically have disparate system resources. Conventional FL, however, adopts a one-size-fits-all solution, where a homogeneous large global model is transmitted to and trained on each client, resulting in an overwhelming workload for less capable clients and starvation for other clients. To address this issue, we propose FedConv, a client-friendly FL framework, which minimizes the computation and memory burden on resource-constrained clients by providing heterogeneous customized sub-models. FedConv features a novel learning-on-model paradigm that learns the parameters of the heterogeneous sub-models via convolutional compression. Unlike traditional compression methods, the compressed models in FedConv can be directly trained on clients without decompression. To aggregate the heterogeneous sub-models, we propose transposed convolutional dilation to convert them back to large models with a unified size while retaining personalized information from clients. The compression and dilation processes, transparent to clients, are optimized on the server leveraging a small public dataset. Extensive experiments on six datasets demonstrate that FedConv outperforms state-of-the-art FL systems in terms of model accuracy (by more than 35% on average), computation and communication overhead (with 33% and 25% reduction, respectively).

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Wang CTang XZhai DZhang RUssipov NZhang Y(2025)Energy-Efficient Federated Learning Through UAV Edge Under Location UncertaintiesIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.348955412:1(223-236)Online publication date: Jan-2025
https://doi.org/10.1109/TNSE.2024.3489554
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https://dl.acm.org/doi/10.1145/3666025.3699338
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Index Terms

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
2. Human-centered computing
  1. Ubiquitous and mobile computing

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MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services

June 2024

778 pages

ISBN:9798400705816

DOI:10.1145/3643832

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Tadashi Okoshi,
JeongGil Ko,
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Published: 04 June 2024

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MOBISYS '24: 22nd Annual International Conference on Mobile Systems, Applications and Services

June 3 - 7, 2024

Minato-ku, Tokyo, Japan

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Cited By

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https://doi.org/10.1109/TNSE.2024.3489554
Liu SJin HTang ZZhai RLu KYu JBai C(2025)Adapter-guided knowledge transfer for heterogeneous federated learningJournal of Systems Architecture10.1016/j.sysarc.2025.103338(103338)Online publication date: Jan-2025
https://doi.org/10.1016/j.sysarc.2025.103338
Yu SXia XZhang ZHou NZheng YShu YLiu JTan RHe YChen J(2024)FDLoRa: Tackling Downlink-Uplink Asymmetry with Full-duplex LoRa GatewaysProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699338(281-294)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699338
Shin YLee KLee SChoi YKim HKo JShu YLiu JTan RHe YChen J(2024)Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight GenerationProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699326(112-125)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699326
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