Sep 17, 2025 · Artificial Intelligence

How Federated Learning Balances Privacy and Collaboration in AI

Federated Learning enables multiple parties to collaboratively train a global AI model without sharing raw data, using techniques like local training, encrypted parameter exchange, and secure aggregation, while addressing privacy, communication efficiency, heterogeneity, and incentive challenges across horizontal, vertical, and transfer learning scenarios.

Federated LearningHorizontal FLSecure Aggregation

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How Federated Learning Balances Privacy and Collaboration in AI

DataFunTalk

Dec 18, 2020 · Artificial Intelligence

Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions

This article explains the evolution of federated learning, contrasts Google’s cross‑device horizontal approach with China’s cross‑silo vertical implementations, analyzes their security vulnerabilities, and demonstrates how secure multi‑party computation—including differential privacy, secure aggregation, and secret‑sharing techniques—can address these challenges while highlighting performance trade‑offs.

Federated LearningSecure Aggregationcross-silo

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Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions