4 citations · 4 across the 7 of their papers we have counts for
8 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…
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…
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…
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
Shahryar Zehtabi, Dong-Jun Han, Rohit Parasnis +2
Decentralized federated learning (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are exclusively carried out by the clients without a central se…
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…
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds
Shahryar Zehtabi, Seyyedali Hosseinalipour, Christopher G. Brinton
A recent emphasis of distributed learning research has been on federated learning (FL), in which model training is conducted by the data-collecting devices. Existing research on FL…