# Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

arXiv: https://arxiv.org/abs/2008.03606 · HTML: https://mlsanity.com/paper/2008.03606/

## Key facts

- Cited by 154 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 30 of 154)

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- [On the Importance and Applicability of Pre-Training for Federated Learning](https://mlsanity.com/paper/2206.11488/) (2022)
- [Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning](https://mlsanity.com/paper/2210.08090/) (2022)
- [Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2102.04761/) (2021)
- [FedExP: Speeding Up Federated Averaging via Extrapolation](https://mlsanity.com/paper/2301.09604/) (2023)
- [Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization](https://mlsanity.com/paper/2310.15080/) (2023)
- [Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion](https://mlsanity.com/paper/2302.03775/) (2023)
- [STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning](https://mlsanity.com/paper/2106.10435/) (2021)
- [Anarchic Federated Learning](https://mlsanity.com/paper/2108.09875/) (2021)
- [Test-Time Robust Personalization for Federated Learning](https://mlsanity.com/paper/2205.10920/) (2022)
- [Momentum Benefits Non-IID Federated Learning Simply and Provably](https://mlsanity.com/paper/2306.16504/) (2023)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction](https://mlsanity.com/paper/2205.13462/) (2022)
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- [FedJAX: Federated learning simulation with JAX](https://mlsanity.com/paper/2108.02117/) (2021)
- [Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning](https://mlsanity.com/paper/2102.03198/) (2021)
- [Local Adaptivity in Federated Learning: Convergence and Consistency](https://mlsanity.com/paper/2106.02305/) (2021)
- [Towards Model Agnostic Federated Learning Using Knowledge Distillation](https://mlsanity.com/paper/2110.15210/) (2021)
- [Momentum Provably Improves Error Feedback!](https://mlsanity.com/paper/2305.15155/) (2023)
- [FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization](https://mlsanity.com/paper/2103.03452/) (2021)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting](https://mlsanity.com/paper/2302.14307/) (2023)
- [TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels](https://mlsanity.com/paper/2207.06343/) (2022)
- [Scaling Language Model Size in Cross-Device Federated Learning](https://mlsanity.com/paper/2204.09715/) (2022)
- [DELTA: Diverse Client Sampling for Fasting Federated Learning](https://mlsanity.com/paper/2205.13925/) (2022)
- [CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning](https://mlsanity.com/paper/2210.02912/) (2022)

## Abstract (arXiv)

Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon. In fact, obtaining an algorithm for FL which is uniformly better than simple centralized training has been a major open problem thus far. In this work, we propose a general algorithmic framework, Mime, which i) mitigates client drift and ii) adapts arbitrary centralized optimization algorithms such as momentum and Adam to the cross-device federated learning setting. Mime uses a combination of control-variates and server-level statistics (e.g. momentum) at every client-update step to ensure that each local update mimics that of the centralized method run on iid data. We prove a reduction result showing that Mime can translate the convergence of a generic algorithm in the centralized setting into convergence in the federated setting. Further, we show that when combined with momentum based variance reduction, Mime is provably faster than any centralized method--the first such result. We also perform a thorough experimental exploration of Mime's performance on real world datasets.