2 papers
cs.LG2026
FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clients
Bostan Khan, Masoud Daneshtalab
Federated learning (FL) must serve devices with varying computational capabilities. A fixed model cannot suit all devices, while training one model per deployment limit is costly.…
cs.LG2026
DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Training
Bostan Khan, Masoud Daneshtalab
Deploying federated learning across heterogeneous IoT device fleets requires tailored neural network architectures for each device class, yet existing Federated Neural Architecture…