7 papers
Transmit Coefficients and Receive Combining Vector Design for OTA-FL with Imperfect CSI
Xiaoyan Ma, Shahryar Zehtabi, Yinan Zou +2
Over-the-air (OTA) computation has recently gained significant attentions as an effective approach to enhance the communication efficiency of wireless federated learning (FL). By e…
Sporadic Gradient Tracking over Directed Graphs: A Theoretical Perspective on Decentralized Federated Learning
Shahryar Zehtabi, Dong-Jun Han, Seyyedali Hosseinalipour +1
Decentralized Federated Learning (DFL) enables clients with local data to collaborate in a peer-to-peer manner to train a generalized model. In this paper, we unify two branches of…
Decentralized Domain Generalization with Style Sharing: Formal Model and Convergence Analysis
Shahryar Zehtabi, Dong-Jun Han, Seyyedali Hosseinalipour +1
Much of federated learning (FL) focuses on settings where local dataset statistics remain the same between training and testing. However, this assumption often does not hold in pra…
Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering
Shahryar Zehtabi, Seyyedali Hosseinalipour, Christopher G. Brinton
Federated learning (FL) is a popular technique for distributing machine learning (ML) across a set of edge devices. In this paper, we study fully decentralized FL, where in additio…
Federated Learning with Ad-hoc Adapter Insertions: The Case of Soft-Embeddings for Training Classifier-as-Retriever
Marijan Fofonjka, Shahryar Zehtabi, Alireza Behtash +2
When existing retrieval-augmented generation (RAG) solutions are intended to be used for new knowledge domains, it is necessary to update their encoders, which are taken to be pret…
Error Analysis for Over-the-Air Federated Learning under Misaligned and Time-Varying Channels
Xiaoyan Ma, Shahryar Zehtabi, Taejoon Kim +1
This paper investigates an OFDM-based over-the-air federated learning (OTA-FL) system, where multiple mobile devices, e.g., unmanned aerial vehicles (UAVs), transmit local machine…