arXiv:2608. 09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities.
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arXiv:2606. 07621v1 Announce Type: cross Abstract: Edge services increasingly use federated learning to personalize on-device models while keeping sensitive data local.
arXiv:2601.15127v4 Announce Type: replace-cross Abstract: Deploying federated learning across heterogeneous IoT device fleets requires tailored neural network architectures for each device class, yet...
arXiv:2608. 07157v1 Announce Type: new Abstract: Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone.
arXiv:2609.06106v1 Announce Type: new Abstract: Heterogeneous Federated Learning (HFL) aims to train models across devices with diverse resource budgets while preserving data privacy. Existing HFL me...
FedSAP is a federated learning framework that addresses heterogeneous edge devices by using structured pruning as a budget-constrained tri-state channel allocation. It partitions model channels into a Global pool, pseudo-domain-specific Private pools, and a Dropped state, allowing broadly useful features to be shared while isolating domain-sensitive updates. Experiments on Digits and Office-Caltech datasets show FedSAP achieving higher mean global accuracy than the strongest baseline while supporting up to 80% client pruning ratios.
arXiv:2606. 27866v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern large models.