13 citations · 17 across the 10 of their papers we have counts for
11 papers
BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models
Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani +3
Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of v…
Federated Foundation Models over Vehicular Networks
Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali +5
This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of…
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…
Graph Theory Meets Federated Learning over Satellite Constellations: Spanning Aggregations, Network Formation, and Performance Optimization
Fardis Nadimi, Payam Abdisarabshali, Jacob Chakareski +2
In this work, we introduce Fed-Span: \textit{\underline{fed}erated learning with \underline{span}ning aggregation over low Earth orbit (LEO) satellite constellations}. Fed-Span aim…
Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR
Fardis Nadimi, Payam Abdisarabshali, Kasra Borazjani +2
Extended reality (XR) systems, which consist of virtual reality (VR), augmented reality (AR), and mixed reality (XR), offer a transformative interface for immersive, multi-modal, a…
Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration
Kasra Borazjani, Payam Abdisarabshali, Fardis Nadimi +5
As embodied AI systems become increasingly multi-modal, personalized, and interactive, they must learn effectively from diverse sensory inputs, adapt continually to user preference…
Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data Distributions
Kasra Borazjani, Payam Abdisarabshali, Naji Khosravan +1
Federated Learning (FL) has emerged as one of the prominent paradigms for distributed machine learning (ML). However, it is well-established that its performance can degrade signif…
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…
Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks
Payam Abdisarabshali, Nicholas Accurso, Filippo Malandra +2
Federated learning (FL) is a popular distributed machine learning (ML) technique in Internet of Things (IoT) networks, where resource-constrained devices collaboratively train ML m…
Decomposition Theory Meets Reliability Analysis: Processing of Computation-Intensive Dependent Tasks over Vehicular Clouds with Dynamic Resources
Payam Abdisarabshali, Minghui Liwang, Amir Rajabzadeh +2
Vehicular cloud (VC) is a promising technology for processing computation-intensive applications (CI-Apps) on smart vehicles. Implementing VCs over the network edge faces two key c…