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Achieving substantial reductions in training latency, AC$^2$P$^2$SL redefines the efficiency of split learning in edge networks without compromising data privacy.
Agentic-LTPO boosts long-term performance by 57.2% in adaptive physical layer systems, revolutionizing how we approach real-time network optimization.
Orthogonal parameter updates and clustered aggregation can slash communication costs by 73% while boosting performance in federated LLM fine-tuning.
