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Federated Learning: Practical Solutions and Value
Introduction to Federated Learning
Federated Learning (FL) is a cutting-edge technology that allows collaborative model training without sharing raw data. It enables organizations and individuals to work together on model development while protecting sensitive data.
Practical Solutions and Value
FL reduces communication costs and integrates diverse datasets while maintaining the unique characteristics of each participant’s data. However, it poses risks of indirect information leakage, especially during model aggregation.
Data Partition Strategies
FL employs various data partition strategies, including Horizontal FL (HFL), Vertical FL (VFL), and Transfer Learning, each with specific advantages:
- Horizontal FL: Suitable for regional branches of the same business aiming to build a richer dataset.
- Vertical FL: Involves non-competing entities with vertically partitioned data sharing overlapping data samples.
- Transfer Learning: Applicable when there is little overlap in data samples and features among multiple subjects with heterogeneous distributions.
Defending Against Label Inference Attacks
To address privacy concerns in FL, researchers at the University of Pavia developed a defense mechanism called KD𝑘 (Knowledge Discovery and 𝑘-anonymity).
KD𝑘 Framework
KD𝑘 relies on Knowledge Distillation (KD) and an obfuscation algorithm to enhance privacy protection. It uses a teacher network to generate soft labels and adds uncertainty through 𝑘-anonymity, making it challenging for attackers to infer the most probable label accurately.
Value and Efficacy
The experimental findings demonstrate a notable reduction in the accuracy of label inference attacks, validating the efficacy of the proposed defense mechanism. The research offers a robust countermeasure tailored to combat label inference attacks and outperforms existing defense strategies.
AI Solutions for Business
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Spotlight on a Practical AI Solution
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