Federated Learning Empowered by Generative Content

Rui Ye, Xinyu Zhu, Jingyi Chai, Siheng Chen, Yanfeng Wang

Primary Area: general machine learning (i.e., none of the above)

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Keywords: federated learning, generative models

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

Abstract:

Federated learning (FL) enables leveraging distributed private data for model training in a privacy-preserving way.
However, data heterogeneity significantly limits the performance of current FL methods.
In this paper, we propose a novel FL framework termed FedGC, designed to mitigate data heterogeneity issues by diversifying private data with generative content.
FedGC is a simple-to-implement framework as it only introduces a one-shot step of data generation.
In data generation, we summarize three crucial and worth-exploring aspects (budget allocation, prompt design, and generation guidance) and propose three solution candidates for each aspect.
Specifically, to achieve a better trade-off between data diversity and fidelity for generation guidance, we propose to generate data based on the guidance of prompts and real data simultaneously.
The generated data is then merged with private data to facilitate local model training.
Such generative data increases the diversity of private data to prevent each client from fitting the potentially biased private data, alleviating the issue of data heterogeneity.
We conduct a systematic empirical study on FedGC, covering diverse baselines, datasets, scenarios, and modalities.
Interesting findings include (1) FedGC consistently and significantly enhances the performance of FL methods, even when notable disparities exist between generative and private data;
(2) FedGC achieves both better performance and privacy-preservation.
We wish this work can inspire future works to further explore the potential of enhancing FL with generative content.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 1145