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Summary of Intelligent Agents For Auction-based Federated Learning: a Survey, by Xiaoli Tang et al.


Intelligent Agents for Auction-based Federated Learning: A Survey

by Xiaoli Tang, Han Yu, Xiaoxiao Li, Sarit Kraus

First submitted to arxiv on: 20 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Computer Science and Game Theory (cs.GT)

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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 survey provides a comprehensive overview of the Intelligent Agents for Auction-based Federated Learning (IA-AFL) literature. It proposes a multi-tiered taxonomy that organizes existing works based on stakeholders served, auction mechanisms adopted, and agent goals. The paper analyzes limitations of existing approaches, summarizes performance evaluation metrics, and discusses promising future directions for stakeholder-oriented decision support in IA-AFL ecosystems.
Low GrooveSquid.com (original content) Low Difficulty Summary
Auction-based federated learning (AFL) helps people share data to train artificial intelligence models. This type of sharing is important because it allows many people to contribute their data without having to give up control over their information. Intelligent agents are computer programs that can make decisions on our behalf. In the context of AFL, these agents help decide who gets rewarded for contributing their data and how those rewards are distributed. Researchers in this field are trying to understand what types of intelligent agents work best and how they can be used to improve the sharing of data.

Keywords

» Artificial intelligence  » Federated learning