FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures
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Abstract (arXiv)
Traditional Federated Learning (FL) faces significant challenges in terms of efficiency and accuracy, particularly in heterogeneous environments where clients employ diverse model architectures and have varying computational resources. Such heterogeneity complicates the aggregation process, leading to performance bottlenecks and reduced model generalizability. To address these issues, we propose FedADP, a federated learning framework designed to adapt to client heterogeneity by dynamically adjusting model architectures during aggregation. FedADP enables effective collaboration among clients with differing capabilities, maximizing resource utilization and ensuring model quality. Our experimental results demonstrate that FedADP significantly outperforms existing methods, such as FlexiFed, achieving an accuracy improvement of up to 23.30%, thereby enhancing model adaptability and training efficiency in heterogeneous real-world settings.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 10 May 2025.
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22 of 33 references.
References · 22
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- 2020Survey of Personalization Techniques for Federated Learningcited by 145
- 2020When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Networkcited by 30
- 2024FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Expertscited by 27
- 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extractioncited by 21
- 2023HeteFedRec: Federated Recommender Systems with Model Heterogeneitycited by 20
- 2020Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-artcited by 19
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- 2022Personalized Federated Learning via Heterogeneous Modular Networkscited by 8
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- 2023Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approachcited by 3
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