Federated Learning for Privacy-Preserving Security Analytics

EasyChair Preprint 14568

Abstract

Federated Learning (FL) has emerged as a promising approach for privacy-preserving security analytics, addressing the growing concern over data privacy and security in the digital age. Traditional machine learning models often require centralized data collection, which raises significant privacy issues and exposes sensitive information to potential breaches. Federated Learning, however, enables collaborative model training across multiple decentralized devices or servers, allowing them to learn from their local data without sharing it directly.

This abstract outlines how Federated Learning enhances privacy-preserving security analytics by aggregating model updates rather than raw data. It highlights the key benefits, including reduced risk of data exposure, improved compliance with data protection regulations, and the ability to leverage vast amounts of distributed data for more robust and generalized security models. Additionally, the abstract discusses challenges such as ensuring model accuracy and efficiency, managing communication overhead, and addressing potential adversarial attacks in a federated setting. The effectiveness of Federated Learning in maintaining data privacy while delivering actionable insights in security analytics represents a significant advancement in safeguarding sensitive information in an increasingly interconnected world.

Keyphrases: Federated Learning, Security Analytics, Traditional machine learning, privacy preserving

@booklet{EasyChair:14568,
  author    = {Favour Olaoye and Axel Egon},
  title     = {Federated Learning for Privacy-Preserving Security Analytics},
  howpublished = {EasyChair Preprint 14568},
  year      = {EasyChair, 2024}}