SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM
Read the original on arXiv Machine Learning →SPADE-DFL is a communication‑efficient decentralized federated learning algorithm that uses a primal–dual method to allow the number of local function‑value updates between neighbor exchanges to increase with the computation budget while maintaining non‑private convergence rates. For smooth nonconvex objectives, it achieves a time‑averaged stationarity and consensus bound of ≠O(T−1/3) with only ≠Theta(T−2/3) communication rounds, where T is the number of local updates per client. The method also supports client‑level differential privacy by isolating data‑dependent increments, proving privacy for the full interactive transcript and quantifying the resulting optimization error, and demonstrates higher mean test accuracy than existing decentralized learning methods on four classification tasks.
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