Summary of Fedbip: Heterogeneous One-shot Federated Learning with Personalized Latent Diffusion Models, by Haokun Chen et al.
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
by Haokun Chen, Hang Li, Yao Zhang, Jinhe Bi, Gengyuan Zhang, Yueqi Zhang, Philip Torr, Jindong Gu, Denis Krompass, Volker Tresp
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address the challenges of One-Shot Federated Learning (OSFL) by proposing a novel approach called Federated Bi-Level Personalization (FedBiP). OSFL requires only a single round of client data or model upload, reducing communication costs and mitigating privacy threats. However, existing methods struggle with client data heterogeneity and limited data quantity when applied to real-world OSFL systems. To overcome these issues, the authors draw upon advancements in Latent Diffusion Models (LDM) that have shown remarkable progress in synthesizing high-quality images through pretraining on large-scale datasets. The key challenge is addressing distribution shifts in synthetic data, which can lead to performance degradation in classification models trained on such data. FedBiP personalizes the pretrained LDM at both instance-level and concept-level, allowing it to synthesize images following the client’s local data distribution without compromising privacy regulations. This approach simultaneously addresses feature space heterogeneity and client data scarcity in OSFL, making it a significant contribution to the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary One-Shot Federated Learning is a new way for machines to learn together without sharing their data. It sounds like a great idea, but there are some challenges that need to be overcome. One of those challenges is that different devices might have different types of data or not enough data at all. To solve this problem, the authors came up with an innovative solution called Federated Bi-Level Personalization. They used something called Latent Diffusion Models, which can create high-quality images from small amounts of data. The key to their approach is personalizing these models so that they work well on different devices and datasets without sharing sensitive information. |
Keywords
» Artificial intelligence » Classification » Federated learning » One shot » Pretraining » Synthetic data
