Authors

* External authors

Venue

Date

Share

Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning

Yue Tan

Chen Chen

Weiming Zhuang

Xin Dong

Lingjuan Lyu

Guodong Long*

* External authors

NeurIPS 2023

2023

Abstract

Federated learning (FL) is an effective machine learning paradigm where multiple clients can train models based on heterogeneous data in a decentralized manner without accessing their private data. However, existing FL systems undergo performance deterioration due to feature-level test-time shifts, which are well investigated in centralized settings but rarely studied in FL. The common non-IID issue in FL usually refers to inter-client heterogeneity during training phase, while the test-time shift refers to the intra-client heterogeneity during test phase. Although the former is always deemed to be notorious for FL, there is still a wealth of useful information delivered by heterogeneous data sources, which may potentially help alleviate the latter issue. To explore the possibility of using inter-client heterogeneity in handling intra-client heterogeneity, we firstly propose a contrastive learning-based FL framework, namely FedICON, to capture invariant knowledge among heterogeneous clients and consistently tune the model to adapt to test data. In FedICON, each client performs sample-wise supervised contrastive learning during the local training phase, which enhances sample-wise invariance encoding ability. Through global aggregation, the invariance extraction ability can be mutually boosted among inter-client heterogeneity. During the test phase, our test-time adaptation procedure leverages unsupervised contrastive learning to guide the model to smoothly generalize to test data under intra-client heterogeneity. Extensive experiments validate the effectiveness of the proposed FedICON in taming heterogeneity to handle test-time shift problems.

Related Publications

How to Evaluate and Mitigate IP Infringement in Visual Generative AI?

ICML, 2025
Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan*, Lingjuan Lyu

The popularity of visual generative AI models like DALL-E 3, Stable Diffusion XL, Stable Video Diffusion, and Sora has been increasing. Through extensive evaluation, we discovered that the state-of-the-art visual generative models can generate content that bears a striking r…

Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models

CVPR, 2025
Jie Ren, Kangrui Chen, Yingqian Cui, Shenglai Zeng, Hui Liu, Yue Xing, Jiliang Tang, Lingjuan Lyu

Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. However, the advancement of T2I diffusion models presents significant risks, as the models could be exploited for malicious purposes, suc…

CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI

CVPR, 2025
Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang, Vikash Sehwag

With the rapid advancement of generative AI, it is now pos-sible to synthesize high-quality images in a few seconds.Despite the power of these technologies, they raise signif-icant concerns regarding misuse. Current efforts to dis-tinguish between real and AI-generated image…

  • HOME
  • Publications
  • Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.