Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
SIAM journal on matrix analysis and applications , volume=
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SignatureTensors.jl is a new Julia package that computes signature tensors of paths, supporting both exact symbolic and numerical computations via compatibility with the OSCAR computer algebra system.
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Adaptive Federated Optimization
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
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SignatureTensors.jl: A Package for Signature Tensors in Julia
SignatureTensors.jl is a new Julia package that computes signature tensors of paths, supporting both exact symbolic and numerical computations via compatibility with the OSCAR computer algebra system.