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FedOpt/FedAdam/FedYogi: adaptive server updates on federated model deltas

Contribution grade

Adaptive federated optimization · Human-designed research method or resource

Reference score125,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Separated client and server optimizers and treated average local model deltas as server pseudo-gradients, instantiating per-coordinate Adagrad, Adam and Yogi-style federated updates.

Scientific scope

Separated client and server optimizers and treated average local model deltas as server pseudo-gradients, instantiating per-coordinate Adagrad, Adam and Yogi-style federated updates. Inherits the underlying optimizers and FedAvg, not their reinvention. Original v1 main analysis assumes full client participation, bounded gradients/variance/heterogeneity and controlled client steps; broader participation extensions are discussed separately. Nonconvex stationarity and empirical gains do not imply unbiased global gradients, arbitrary tuning robustness or global optima.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits the underlying optimizers and FedAvg, not their reinvention. Original v1 main analysis assumes full client participation, bounded gradients/variance/heterogeneity and controlled client steps; broader participation extensions are discussed separately. Nonconvex stationarity and empirical gains do not imply unbiased global gradients, arbitrary tuning robustness or global optima.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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