Summary of Formal Logic Enabled Personalized Federated Learning Through Property Inference, by Ziyan An et al.
Formal Logic Enabled Personalized Federated Learning Through Property Inference
by Ziyan An, Taylor T. Johnson, Meiyi Ma
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 Medium Difficulty Summary: Recent advancements in federated learning (FL) have enabled the development of decentralized collaborative applications in Artificial Intelligence of Things (AIoT). However, current research lacks the ability to enable data-driven client models with symbolic reasoning capabilities. This work proposes a new training paradigm that leverages temporal logic reasoning to address the issue of heterogeneous client devices. Our approach incorporates mechanically generated logic expressions for each FL client and introduces aggregation clusters and a partitioning algorithm to group clients based on their temporal reasoning properties. We evaluate our method on two tasks: traffic volume prediction using sensory data from fifteen states, and smart city multi-task prediction utilizing synthetic data. The evaluation results show clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about making machines learn together without sharing their personal data. They want to enable these learning machines to think logically and make good predictions. The problem is that each machine has its own way of thinking, which makes it hard for them to work together effectively. The researchers propose a new approach that helps the machines understand each other’s logical reasoning patterns. This allows them to learn better and make more accurate predictions. They tested this approach on two real-world tasks: predicting traffic volume based on data from 15 states and making predictions about smart cities using synthetic data. The results show significant improvements, with accuracy increasing by up to 54%. |
Keywords
* Artificial intelligence * Federated learning * Multi task * Synthetic data
