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Summary of Fedsi: Federated Subnetwork Inference For Efficient Uncertainty Quantification, by Hui Chen et al.


FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification

by Hui Chen, Hengyu Liu, Zhangkai Wu, Xuhui Fan, Longbing Cao

First submitted to arxiv on: 24 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

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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 proposes a novel federated learning (FL) framework, called FedSI, which addresses the issue of efficient systematic uncertainty quantification in personalized FL. FedSI is based on Bayesian deep neural networks (DNNs) and leverages their ability to incorporate uncertainties effectively. The framework implements a client-specific subnetwork inference mechanism, selecting network parameters with large variance to be inferred through posterior distributions, while fixing the rest as deterministic ones. This approach enables fast and scalable inference while preserving systematic uncertainties. FedSI outperforms existing FL baselines in heterogeneous scenarios on three benchmark datasets.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper develops a new way for computers to learn together using different types of data. They call it FedSI, which is short for “federated subnetwork inference”. It’s like having many small versions of the same computer program, each one special and unique. The new method helps figure out what parts of the program need more attention by looking at how uncertain they are. This allows the computers to work together better when their data is different. Tests showed that this approach works well on three different types of datasets.

Keywords

» Artificial intelligence  » Attention  » Federated learning  » Inference