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By eliminating bidirectional communication, L-shaped SFT allows edge devices to participate in fine-tuning without the need for constant connectivity, drastically cutting down on communication overhead.
SplitLite slashes communication costs in federated learning by up to 93.5% without sacrificing performance, revealing a hidden structure in model training data.
Achieving global optimality in model splitting for split federated learning could revolutionize resource allocation strategies in edge computing environments.