Abstract
Federated Learning Is A Training Framework That Enables Multiple Participants To Collaboratively Train A Shared Model While Preserving Data Privacy. The Heterogeneity Of Devices And Networking Resources Of The Participants Delay The Training And Aggregation. The Paper Introduces A Novel Approach To Federated Learning By Incorporating Resource-Aware Clustering. This Method Addresses The Challenges Posed By The Diverse Devices And Networking Resources Among Participants. Unlike Static Clustering Approaches, This Paper Proposes A Dynamic Method To Determine The Optimal Number Of Clusters Using Dunn Indices. It Enables Adaptability To The Varying Heterogeneity Levels Among Participants, Ensuring A Responsive And Customized Approach To Clustering. Next, The Paper Goes Beyond Empirical Observations By Providing A Mathematical Derivation Of The Communication Rounds For Convergence Within Each Cluster. Further, The Participant Assignment Mechanism Adds A Layer Of Sophistication And Ensures That Devices And Networking Resources Are Allocated Optimally. Afterwards, We Incorporate A Master-Slave Technique, Particularly Through Knowledge Distillation, Which Improves The Performance Of Lightweight Models Within Clusters. Finally, Experiments Are Conducted To Validate The Approach And To Compare It With State-Of-The-Art. The Results Demonstrated An Accuracy Improvement Of Over 3% Compared To Its Closest Competitor And A Reduction In Communication Rounds Of Around 10%.
Recommended Citation
R. Mishra et al., "Resource Aware Clustering For Tackling The Heterogeneity Of Participants In Federated Learning," IEEE Transactions on Parallel and Distributed Systems, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/TPDS.2024.3379933
Department(s)
Computer Science
Publication Status
Early Access
Keywords and Phrases
Adaptation models; Computational modeling; Federated learning; Federated learning; Heterogeneity; Master-slave technique; Mathematical models; Performance evaluation; Resource aware clustering; Servers; Training
International Standard Serial Number (ISSN)
1558-2183; 1045-9219
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
