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CED-EF achieves faster convergence in decentralized optimization while using significantly less communication bandwidth, reshaping the landscape of multi-agent learning.
Achieving linear speedup in decentralized stochastic optimization without unrolling dynamics could revolutionize how we approach distributed learning in dynamic environments.
