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Award ID contains: 2106891

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  1. ; ; ; ; (, IEEE)
    Full Text Available 
  2. ; ; (, IEEE/ACM Transactions on Networking)
    Full Text Available 
  3. ; ; ; ; (, IEEE)
    Full Text Available 
  4. ; ; ; ; ; (, ACM)
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  5. ; ; ; ; ; (, 2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS))
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  6. ; ; ; ; ; ; ; (, ACM)
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  7. ; ; ; ; ; ; ; ; (, 6th Conference on Machine Learning and Systems)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; (, 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON))
    Accepted Manuscript Full Text Available
  9. ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution. We relax this hard association assumption to soft clustered federated learning, which allows every local dataset to follow a mixture of multiple source distributions. We propose FedSoft, which trains both locally personalized models and high-quality cluster models in this setting. FedSoft limits client workload by using proximal updates to require the completion of only one optimization task from a subset of clients in every communication round. We show, analytically and empirically, that FedSoft effectively exploits similarities between the source distributions to learn personalized and cluster models that perform well. 
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  10. ; ; ; ; ; (, Proceedings of The 25th International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available