Scalytics Connect Blogs With The Topic: Federated Learning

Federated Learning Security: Attacks, Vulnerabilities & Defenses

Federated learning isn't automatically secure. Our research identifies key vulnerabilities including model poisoning, gradient leakage, and membership inference attacks. Learn defense strategies like differential privacy, secure aggregation, and Byzantine-resilient protocols for production FL systems.

Author:

Scalytics

Federated Learning Framework: Scalable Enterprise AI

Production federated learning requires more than research prototypes. Our FL framework handles 1000+ participants, heterogeneous data distributions, and enterprise security requirements. Includes deployment patterns for healthcare consortiums, financial networks, and cross-border collaborations.

Author:

Scalytics

LLMs + Federated Learning: Enterprise AI Architecture

Enterprise LLM deployment fails when training data is siloed across departments, regions, or subsidiaries. Federated learning enables collaborative LLM fine-tuning without data centralization—essential for regulated industries needing domain-specific AI while maintaining compliance.

Author:

Alexander Alten

In-Situ Processing: Train AI Where Data Lives

Moving petabytes to train AI is expensive, slow, and often illegal under GDPR. In-situ federated processing brings computation to data—training models across distributed sources without centralization. Reduce cloud egress costs by 60% while achieving compliance by design.

Author:

Alexander Alten

Sustainable AI: Cut Carbon Footprint with Federated Learning

Training GPT-4 emitted 500+ tons of CO2. Federated learning reduces AI's carbon footprint by eliminating massive data transfers and enabling edge processing. Learn how sustainable AI practices through distributed training align environmental goals with compliance requirements.

Author:

Vatsal Shah

Federated Data Management: Engineer's Complete Guide

Federated data management is becoming essential for data engineers. This technical guide covers federation patterns, query optimization across distributed sources, consistency models, and implementation with Apache Wayang—skills increasingly required for enterprise data platforms.

Author:

Alexander Alten

Membership Inference Attacks: AI Privacy Risks & Solutions

Can attackers tell if your customer data trained a model? Membership inference attacks expose serious privacy risks in centralized AI. Learn how federated learning with differential privacy prevents data leakage while maintaining model accuracy for sensitive healthcare and financial applications.

Author:

Vatsal Shah

Reduce AI Bias with Federated Learning

AI trained on centralized data inherits the biases of whoever collected it. Federated learning enables model training across diverse populations and regions—reducing demographic bias while maintaining privacy. Essential for fair AI in healthcare diagnostics, lending decisions, and hiring systems.

Author:

Alexander Alten

Poisoning Attacks in Federated Learning: Detection Guide

Poisoning attacks are the biggest threat to federated learning systems. Malicious participants can corrupt shared models through targeted data poisoning or model update manipulation. This guide covers attack vectors, detection algorithms, and Byzantine-resilient aggregation methods.

Author:

Dr. Euijin Alley Choo

Federated Learning Adoption: Industry Trends & Frameworks

Federated learning adoption is accelerating across regulated industries. This analysis covers adoption patterns in healthcare (cross-hospital AI), finance (fraud detection consortiums), and manufacturing (predictive maintenance networks)—plus framework comparison of Flower, PySyft, and TensorFlow Federated.

Author:

Alexander Alten

Federated Learning Guide: Privacy-Preserving AI Training

Federated learning enables AI model training across hospitals, banks, and government agencies without exposing raw data. This comprehensive guide covers FL architectures, security considerations, and step-by-step implementation for HIPAA, GDPR, and DORA compliance.

Author:

Alexander Alten