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· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 일반
· ISBN : 9781032471631
· 쪽수 : 362쪽
· 출판일 : 2025-06-26
목차
Introduction to Federated Learning: Methods, and Classifications. Go Local, Go Global and Go Fusion - How to pick data from various contexts. Federated Learning Architectures, Opportunities, and Applications. Secure and Private Federated Learning through Encrypted Parameter Aggregation. Navigating Privacy Concerns in Federated Learning: A GDPR-Focused Analysis. A Federated Learning Approach for Resource-Constrained IoT Security Monitoring. Efficient Federated Learning Techniques for Data Loss Prevention in Cloud Environment. Maximizing Fog Computing Efficiency with Federated Multi-Agent Deep Reinforcement Learning. Future of Medical Research with a data-driven Federated Learning Approach. Collaborative Federated Learning in Healthcare Systems. Federated Learning for Efficient Cardiac Disease Prediction based on Hyper Spectral Feature Selection using Deep Spectral Convolution Neural Network. A Federated Learning based Alzheimer’s Disease Prediction. Detecting Device Sensors of Luxury Hotel Using Blockchain Based Federated Learning to Increase Customer Satisfaction. Navigating the Complexity of Macro-Tasks: Federated Learning as a Catalyst for Effective Crowd Coordination. Stock Market Prediction via Twitter Sentiment Analysis using BERT: A Federated Learning Approach.















