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Orthogonal parameter updates and clustered aggregation can slash communication costs by 73% while boosting performance in federated LLM fine-tuning.
Achieve Byzantine-robust, privacy-preserving federated learning without sacrificing convergence speed by letting each device autonomously optimize its neighbor selection using a GNN-RL agent.
