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

0 likes · 24 min read

How Federated Learning Balances Privacy and Collaboration in AI

DataFunSummit

Dec 28, 2022 · Artificial Intelligence

Federated Learning in Advertising: Business Background, Conversion Flow, Algorithmic Techniques, Vertical & Horizontal FL, and Security

This article explains how federated learning is applied to the advertising industry, covering business background, conversion processes from user, client, and server perspectives, algorithmic components such as CTR and CVR models, vertical and horizontal federated learning architectures, compression techniques, and security challenges with corresponding defenses.

AdvertisingConversion TrackingHorizontal FL

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Federated Learning in Advertising: Business Background, Conversion Flow, Algorithmic Techniques, Vertical & Horizontal FL, and Security

DataFunTalk

Aug 28, 2022 · Artificial Intelligence

Federated Learning in Advertising: Business Background, Conversion Flow, Algorithm Techniques, Vertical & Horizontal FL, and Security

This presentation by Huawei federated‑learning expert Liu Lu explains how federated learning can be applied to the advertising ecosystem, covering business background, multi‑perspective conversion processes, core ad‑ranking algorithms, vertical and horizontal federated learning architectures, and the associated attack‑defense techniques.

AdTechHorizontal FLVertical FL

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Federated Learning in Advertising: Business Background, Conversion Flow, Algorithm Techniques, Vertical & Horizontal FL, and Security

DataFunSummit

Aug 7, 2022 · Artificial Intelligence

Vertical Federated Learning: Characteristics, Research Directions, and Performance Optimization

This article introduces federated learning, traces its evolution, compares horizontal and vertical federated learning, analyzes the unique computational traits of vertical FL, and presents practical performance‑optimization techniques such as offline computation, sparse‑data handling, communication compression, and homomorphic encryption integration.

Federated LearningPrivacy ComputingVertical FL

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Vertical Federated Learning: Characteristics, Research Directions, and Performance Optimization

DataFunTalk

Aug 24, 2021 · Artificial Intelligence

De‑identification in Federated Learning: Using xID Technology to Protect Sample Intersection Information

This article explains federated learning, its vertical and horizontal variants, the privacy risks of sample intersection in financial scenarios, reviews common de‑identification methods, and introduces the xID technique with generation‑mapping services to securely protect intersecting data while enabling collaborative AI modeling.

Data De-identificationFederated LearningVertical FL

0 likes · 13 min read

De‑identification in Federated Learning: Using xID Technology to Protect Sample Intersection Information