2 citations · 2 across the 4 of their papers we have counts for
4 papers
From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
Allan Salihovic, Payam Abdisarabshali, Michael Langberg +1
We provide our perspective on X-Learning (XL), a novel distributed learning architecture that generalizes and extends the concept of decentralization. Our goal is to present a visi…
Hierarchical Federated Foundation Models over Wireless Networks for Multi-Modal Multi-Task Intelligence: Integration of Edge Learning with D2D/P2P-Enabled Fog Learning Architectures
Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani +6
The rise of foundation models (FMs) has reshaped the landscape of machine learning. As these models continued to grow, leveraging geo-distributed data from wireless devices has bec…
Competitive Analysis of Arbitrary Varying Channels
Michael Langberg, Oron Sabag
Arbitrary varying channels (AVC) are used to model communication settings in which a channel state may vary arbitrarily over time. Their primary objective is to circumvent statisti…
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection
Payam Abdisarabshali, Kwang Taik Kim, Michael Langberg +2
Existing studies on federated learning (FL) are mostly focused on system orchestration for static snapshots of the network and making static control decisions (e.g., spectrum alloc…