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Monolithic federated updates are a fundamental bottleneck for embodied AI: decoupling vision, language, and action streams cuts uplink payloads by 96% and beats standard FedAvg by 22 percentage points under real-world wireless interference.
Achieving up to 21.58% equal error rates, this method outperforms traditional approaches by effectively disentangling speaker identity from synthesis artifacts.
