22 citations · 26 across the 8 of their papers we have counts for
9 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…
Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay +2
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, thei…
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…
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…
FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting
Kasra Borazjani, Kiarash Kianfar, Seyyedali Hosseinalipour +1
Authorization systems are increasingly relying on processing radio frequency (RF) waveforms at receivers to fingerprint (i.e., determine the identity of) the corresponding transmit…
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities
Kasra Borazjani, Naji Khosravan, Leslie Ying +1
The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative fe…