Summary of Task Diversity in Bayesian Federated Learning: Simultaneous Processing Of Classification and Regression, by Junliang Lyu et al.
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
by Junliang Lyu, Yixuan Zhang, Xiaoling Lu, Feng Zhou
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a critical limitation in current federated learning approaches by proposing a novel integration of multi-task learning and federated learning. The authors develop a Bayesian non-parametric approach, using multi-output Gaussian processes (MOGP), to handle correlated classification and regression tasks on local devices. At the global level, the central server aggregates posteriors from local devices, updating a prior redistributed for training local models until convergence. The paper utilizes Pólya-Gamma augmentation and mean-field variational inference to enhance computational efficiency and convergence rate. Experimental results demonstrate superior predictive performance, out-of-distribution detection, uncertainty calibration, and convergence rate on both synthetic and real data, highlighting the method’s potential in diverse applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a way for many devices to learn together (federated learning) by letting them work on different tasks. Right now, most federated learning approaches only focus on one type of task, but this paper shows that it can handle many types of tasks at once. The authors use a special kind of math called Gaussian processes to make this happen. They also figure out how to make the math work faster and better on each device. The results show that this new way works really well for predicting things, detecting when something is wrong, and understanding uncertainty. |
Keywords
» Artificial intelligence » Classification » Federated learning » Inference » Multi task » Regression
