Data-driven decision-making is a key enabler of intelligent enterprises, yet centralized machine learning (ML) poses privacy and efficiency risks. Federated learning (FL) offers a decentralized solution. Still, it faces 1) high communication cost, 2) vulnerability to poisoning and inference attacks, and 3) degraded per...
Reza Fotohi, F. S. Aliee, B. Farahani· IEEE Transactions on Cogniti...· 0 citations
Multi-Agent Systems (MAS) enable autonomous agents to reason, collaborate, and interact with complex environments. Their increasing autonomy and interdependence introduce system-level trustworthiness challenges that cannot be addressed by securing individual agents alone, as failures originating at lower system layers...
B. Farahani, Mohammad Rouintan, Parichehr Vahidinia et al.· Coins· 0 citations
Falling launch costs, together with advances in radiation-tolerant electronics, optical inter-satellite links, and high-efficiency power and thermal systems, have made Space Data Centers (SDCs) an increasingly credible proposition, whether for deep-space autonomy, on-board processing of Earth-observation data, or as or...
Soyed Tuhin Ahmed, B. Farahani, Krishnendu Chakrabarty et al.· Coins· 0 citations
Experimental results show that routing-aware collaboration consistently improves personalized performance compared to conventional federated averaging and local training, while maintaining the same communication cost, and shows that client-centric and expert-centric clustering provides an effective and scalable approac...
Ankita Sharma, B. Farahani, S. Moosavi et al.· Coins· 0 citations
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