Summary of Enhancing Federated Learning with Adaptive Differential Privacy and Priority-based Aggregation, by Mahtab Talaei et al.
Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation
by Mahtab Talaei, Iman Izadi
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 In this paper, researchers develop a novel approach to federated learning that prioritizes privacy while addressing heterogeneities in devices’ capabilities. They propose a personalized differential privacy framework that injects noise based on clients’ relative impact factors and aggregates parameters considering device-specific properties. This innovative method ensures efficient preservation of privacy by analyzing the convergence boundary of the FL algorithm and studying its property with adaptive impact factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a new way to develop global models without sharing local data. However, this can be risky because model updates can reveal sensitive information. To fix this, researchers use differential privacy to add noise to the parameters. But, devices have different capabilities, which can cause problems and slow down the process. This paper proposes a personalized way to make sure everything works together smoothly while keeping things private. |
Keywords
» Artificial intelligence » Federated learning
