7 papers
A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints
Krishnendu S. Tharakan, Carlo Fischione
Decentralized learning systems aim to collaboratively train models across multiple clients without relying on a central coordinator. While decentralization improves scalability, pr…
Reliability-Aware Scheduling for Digital Twin Maintenance
Milica Jankov, Carlo Fischione
In Industrial Internet of Things systems, learning-enabled Digital Twins (DTs) support remote monitoring by using data reported by distributed devices to maintain digital represent…
Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learning
Amirreza Kazemi, Seyed Mohammad Azimi-Abarghouyi, Gabor Fodor +1
Hierarchical federated learning (HFL) is well suited for large-scale wireless and Internet of Things systems, where devices communicate with nearby edge servers before reaching the…
Decentralized Fairness Aware Multi Task Federated Learning for VR Network
Krishnendu S. Tharakan, Carlo Fischione
Wireless connectivity promises to unshackle virtual reality (VR) experiences, allowing users to engage from anywhere, anytime. However, delivering seamless, high-quality, real-time…
Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
Seyed Mohammad Azimi-Abarghouyi, Carlo Fischione, Kaibin Huang
Over-the-Air Federated Learning (AirFL) is an emerging paradigm that tightly integrates wireless signal processing and distributed machine learning to enable scalable AI at the net…
Majority Vote Compressed Sensing
Henrik Hellström, Jiwon Jeong, Ayfer Özgür +2
We consider the problem of non-coherent over-the-air computation (AirComp), where devices carry high-dimensional data vectors of sparsity $\lVert\…