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Experimental results show that the proposed Q-learning-based adaptive retraining approach effectively reduces retraining overhead compared to greedy and random baselines, while maintaining system performance within predefined limits.
DMTT (Dynamic MURMURA with Trusted Topology), a decentralized personalized FL protocol built on MURMURA, which uses evidential deep learning to down-weight distribution-mismatched peers, extended here to time-varying graphs under topology-manipulation attacks.
A two-stage predictive framework in which a Spectral Temporal Graph Neural Network (StemGNN) predicts future user equipment (UE) scheduling states from delayed historical observations, and the predictions replace stale inputs to the CBF-SLNR precoder is proposed.
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