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This paper presents an adaptive joint compression and synchronization mechanism for federated split learning to reduce communication overhead in IoT rainfall prediction, achieving significant traffic reduction without major loss in predictive quality.
This paper introduces PACT, a method for structuring agent-to-agent communication in multi-agent LLM systems that uses compact action-state records to reduce token consumption while maintaining or improving task performance, with demonstrated gains on SWE-agent and OpenHands.
This paper proposes CE-FedGNN, a federated graph neural network framework that achieves communication efficiency and privacy preservation by infrequently exchanging aggregated node representations with metric differential privacy guarantees, and demonstrates strong performance on benchmarks.