Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critical bottleneck. Furthermore, applying Low-Rank Adaptation (LoRA) in FL faces a fundamental “aggregation dilemma” between the accurate Sum-of-Products (SoP) and the commun...
Han Zou, Chao Zhang, Yu-Zhi Yang et al.· IEEE Journal on Selected Are...· 0 citations
The TelecomGPT-R1 family of open source unified telecom reasoning models structured around four complementary axes: protocol, knowledge, modeling, and fault are introduced, and supervised fine-tuning instills telecom knowledge and evidence-grounded reasoning patterns to overcome the cold start barrier for reinforcement...
Bo-Hao Wang, Chen-Wei Wu, Han Zou et al.· 1 citation
The PhysAI-Bench is introduced, a benchmark for evaluating the agentic decision-making required for reliable autonomy in Physical AI, which contains 10,178 standardized decision instances automatically extracted from conversational traces of autonomous UAV missions.
M. Ferrag, Mérouane Debbah, Abderrahmane Lakas et al.· 0 citations
UAVBench establishes a reproducible, physically grounded foundation for benchmarking agentic AI in autonomous aerial systems and advancing next-generation UAV reasoning intelligence, and presents UAVBench_MCQ, a reasoning-oriented extension containing 50,000 multiple-choice questions spanning ten cognitive and ethical...
M. Ferrag, Abderrahmane Lakas, Mérouane Debbah· IEEE Open Journal of Vehicul...· 16 citations
MulRobBench provides a reproducible benchmark for trustworthy multimodal UAV decision making under realistic operational constraints and identifies modality-trust selection, constraint extraction, glare, missing data, and operator shorthand as the primary causes of decision instability.
B. Alsinglawi, Wei-Zheng Wang, Jun-Yi Wu et al.· arXiv.org· 0 citations
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