mlsanityData to 7 Oct 2026

An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy

arXiv 2503.20768 ↗First posted 26 Mar 2025cs.LG cs.DCcited by 0

Not on the map. Why?

Abstract (arXiv)

Federated Learning (FL) enables distributed ML model training on private user data at the global scale. Despite the potential of FL demonstrated in many domains, an in-depth view of its impact on model accuracy remains unclear. In this paper, we investigate, systematically, how this learning paradigm can affect the accuracy of state-of-the-art ML models for a variety of ML tasks. We present an empirical study that involves various data types: text, image, audio, and video, and FL configuration knobs: data distribution, FL scale, client sampling, and local and global computations. Our experiments are conducted in a unified FL framework to achieve high fidelity, with substantial human efforts and resource investments. Based on the results, we perform a quantitative analysis of the impact of FL, and highlight challenging scenarios where applying FL degrades the accuracy of the model drastically and identify cases where the impact is negligible. The detailed and extensive findings can benefit practical deployments and future development of FL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 60 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

24 of 60 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
  8. 2020Adaptive Federated Optimizationcited by 1,036
  9. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  10. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  11. 2016Deep Pyramidal Residual Networkscited by 163
  12. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 124
12 more
  1. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  2. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  3. 2021Improving Fairness for Data Valuation in Horizontal Federated Learningcited by 22
  4. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  5. 2023Efficient and Secure Federated Learning for Financial Applicationscited by 17
  6. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  7. 2024Poisoning Federated Recommender Systems with Fake Userscited by 15
  8. 2024Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Trainingcited by 14
  9. 2022Communication-Efficient Robust Federated Learning with Noisy Labelscited by 11
  10. 2021FedMood: Federated Learning on Mobile Health Data for Mood Detectioncited by 10
  11. 2023ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacycited by 9
  12. 2022Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacycited by 7

Built on by

Not cited yet.