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      "statement": "# FedOpt/FedAdam/FedYogi: adaptive server updates on federated model deltas\n\nSeparated client and server optimizers and treated average local model deltas as server pseudo-gradients, instantiating per-coordinate Adagrad, Adam and Yogi-style federated updates.\n\n## Scientific scope\n\nInherits 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.\n\n## People and systems\n\nSashank J. Reddi; Zachary Charles; Manzil Zaheer; Zachary Garrett; Keith Rush; Jakub Konečný; Sanjiv Kumar; H. Brendan McMahan\n\n\n\n## Original sources\n\n- [Adaptive Federated Optimization](https://arxiv.org/abs/2003.00295v1)\n",
      "summary": "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.",
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