Sharp Gaussian approximations for Decentralized Federated Learning

2025-05-12 · Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu

Federated Learning has gained traction in privacy-sensitive collaborative environments, with local SGD emerging as a key optimization method in decentralized settings. While its convergence properties are well-studied, asymptotic statistical guarantees beyond convergence remain limited. In this paper, we present two generalized Gaussian approximation results for local SGD and explore their implications. First, we prove a Berry-Esseen theorem for the final local SGD iterates, enabling valid multiplier bootstrap procedures. Second, motivated by robustness considerations, we introduce two distinct time-uniform Gaussian approximations for the entire trajectory of local SGD. The time-uniform approximations support Gaussian bootstrap-based tests for detecting adversarial attacks. Extensive simulations are provided to support our theoretical results.

📄 PDF Abstract BibTeX arXiv:2505.08125

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learningvalid

Methods 이 논문이 사용한 방법론

SGD Stochastic Gradient Descent is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using…

Local SGD Local SGD is a distributed training technique that runs SGD independently in parallel on different workers and averages the sequences…

F3DGS: Federated 3D Gaussian Splatting for Decentralized Multi-Agent World Modeling

2026-04-02 · Morui Zhu, Mohammad Dehghani Tezerjani, Mátyás Szántó, Márton Vaitkus 외 arxiv

We present F3DGS, a federated 3D Gaussian Splatting framework for decentralized multi-agent 3D reconstruction. Existing 3DGS pipelines assume centralized access to all observations, which limits their applicability in di…

Distributed Optimization 3D Reconstruction Point Clouds

From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching

2024-04-27 · Nannan Wu, Zhuo Kuang, Zengqiang Yan, Li Yu

Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is challenging to ensure consistent imagin…

Fairness Federated Learning

Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations

2024-05-25 · Conor Hassan, Joshua J Bon, Elizaveta Semenova, Antonietta Mira 외

Federated learning (FL) allows for collaborative model training across decentralized clients while preserving privacy by avoiding data sharing. However, current FL methods assume conditional independence between client m…

Decision Making Epidemiology Federated Learning Gaussian Processes

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

2026-07-05 · Liyang Yuan, Yibo Yang, Dandan Guo arxiv

Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centraliz…

Federated Learning

Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning

2022-03-06 · George P. Kontoudis, Daniel J. Stilwell

In this paper, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. To decentralize the implementation of GP training optimization algorithms, we empl…

Federated Learning Gaussian Processes