Summary of Fedbaf: Federated Learning Aggregation Biased by a Foundation Model, By Jong-ik Park et al.
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
by Jong-Ik Park, Srinivasa Pranav, José M. F. Moura, Carlee Joe-Wong
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
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 Foundation models are now central to major tech companies due to their ability to generalize across diverse tasks. The current methods for adapting these foundation models to new applications often rely on Federated Learning (FL) and disclose the foundation model weights to clients when initializing the global model. However, these methods prioritize client data privacy over model and information security. This paper introduces Federated Learning Aggregation Biased by a Foundation Model (FedBaF), a novel method that integrates pre-trained foundation model weights during the FL aggregation phase while preserving confidentiality. Unlike traditional methods, FedBaF leverages the power of foundation models to train more accurate models in non-IID and adversarial scenarios. The paper’s comprehensive experiments use Pre-ResNet and foundation models like Vision Transformer to demonstrate that FedBaF not only matches but often surpasses the test accuracy of traditional weight initialization methods by up to 11.4% in IID settings and up to 15.8% in non-IID settings. Additionally, FedBaF applied to a Transformer-based language model significantly reduced perplexity by up to 39.2%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers learn faster and better without sharing secrets. Right now, big tech companies are using something called foundation models that can do many tasks well. The problem is that current ways of adapting these models for new uses might be safe for personal data but not very secure overall. To fix this, the authors invented a new method called FedBaF. It lets computers use the power of foundation models to learn better while keeping their secrets safe. The paper shows that FedBaF works well in different situations and even improves language model accuracy by up to 39%. |
Keywords
» Artificial intelligence » Federated learning » Language model » Perplexity » Resnet » Transformer » Vision transformer
