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This paper addresses the challenges of federated learning (FL) in dynamic environments where clients can join or leave the system, affecting the optimization goals and model adaptation. It proposes a probabilistic framework to model client types and their data distributions, along with an adaptive initial model construction strategy to enhance performance amidst client variability. Experimental results demonstrate the effectiveness of the proposed approach across various datasets and FL algorithms, emphasiz

Author

nazzy.2603

Language

EN