Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning (2008.03606v2)

Abstract: 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.

• The paper introduces Mime, an algorithmic framework that mimics centralized optimizers to mitigate client drift in federated learning.
• It employs control variates and a global optimizer state to correct gradient bias and enhance local update effectiveness.
• Empirical analyses on benchmarks like EMNIST and Shakespeare show Mime outperforms methods like FedAvg and SCAFFOLD in convergence and accuracy.

Overview of "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning"

The paper, "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning," addresses the significant challenges faced in optimizing federated learning (FL) frameworks, particularly those arising from client data heterogeneity and the client drift phenomenon. Federated learning aims to train models across a large number of decentralized devices or clients holding private data samples without transmitting their data to a central server. A notable difficulty in this setup is client drift, where updates from individual clients biased by local data distributions can mislead the global model convergence.

Core Contributions and Methodology

The paper introduces Mime, a novel algorithmic framework designed to adapt and improve centralized optimization techniques for FL settings. Mime's core innovation lies in its ability to mirror centralized methods such as Momentum SGD and Adam, within the decentralized, cross-device federated learning schema. The algorithm mitigates client drift by employing:

1. Control Variates: Used to correct the bias in gradients computed at each client.
2. Global Optimizer State: A fixed global state, such as a momentum accumulator, applied during each local client update to prevent overfitting to local data peculiarities.

The paper establishes a formal reduction theory, translating the convergence of centralized algorithms into the federated learning context. Remarkably, it includes an analysis showing that Mime, when combined with momentum-based variance reduction (MVR), is faster than any traditional centralized method—a significant theoretical milestone.

Theoretical and Empirical Analysis

Mime is rigorously analyzed in both theoretical and empirical frameworks. Key results include:

• Convergence Analysis: The paper proves that Mime can achieve convergence rates comparable to centralized methods while addressing client drift effectively. The convergence analysis relies on mitigating variance via momentum and exploiting Hessian similarity between the server and clients.
• Statistical and Optimization Improvements: Under certain mild heterogeneity assumptions on client data, Mime is shown to outperform server-only methods by making judicious use of local updates and incorporating momentum efficiently.

Empirical evaluations conducted using benchmarks like EMNIST, Shakespeare, and StackOverflow illustrate Mime's practical benefits. The performance improvements manifest in faster convergence rates and higher accuracy compared to existing federated algorithms like FedAvg and SCAFFOLD.

Implications and Future Directions

Mime potentially transforms federated learning by allowing the direct application of robust centralized optimizations to distributed and private data settings. The framework is pivotal for privacy-focused training on widespread devices, such as mobile phones, where communication is a bottleneck, and computations are constrained.

The research provides a promising avenue for future AI developments:

• Enhanced FL Models: Mime sets the groundwork for incorporating advanced optimizers into FL with minimal client-server interaction.
• Scalability and Adaptivity: The algorithms' adaptability to varying data distributions opens pathways for more scalable machine learning systems in a wide array of applications, including personalized healthcare and edge computing.
• Beyond FL: The insights gleaned from Mime could influence decentralized and distributed learning paradigms, addressing communication challenges and data heterogeneity. Further exploration into incorporating differential privacy and byzantine robustness could expand Mime’s applicability to more domains and use cases.

Mime paves the way for efficient, privacy-preserving distributed machine learning, aligning closely with future needs in the AI landscape.