
BOOKS - Federated Learning From Algorithms to System Implementation

Federated Learning From Algorithms to System Implementation
Author: Liefeng Bo, Heng Huang, Songxiang Gu, Yanqing Chen
Year: 2025
Pages: 546
Format: PDF
File size: 24.9 MB
Language: ENG

Year: 2025
Pages: 546
Format: PDF
File size: 24.9 MB
Language: ENG

Federated Learning From Algorithms to System Implementation The world is rapidly changing, and technology is evolving at an unprecedented rate. The development of modern knowledge has led to significant advancements in various fields, from healthcare to transportation, and everything in between. However, this rapid pace of change can be overwhelming, making it challenging for individuals to keep up with the latest innovations. In his book "Federated Learning From Algorithms to System Implementation author Liefeng Bo, Heng Huang, Songxiang Gu, Yanqing Chen explores the importance of understanding the process of technology evolution and its impact on society. The book begins by discussing the concept of federated learning, which is a decentralized approach to machine learning that enables multiple parties to collaboratively train models without sharing their data. This approach has become increasingly popular due to privacy concerns and the need for secure data sharing. The author delves into the technical aspects of federated learning, explaining how it works and its potential applications in various industries. As the book progresses, the author shifts the focus to the broader implications of technology evolution on society. He argues that the rapid pace of technological advancements has created a sense of urgency among individuals and organizations, leading to a constant need for adaptation. This urgency has resulted in a fragmented society, where people are often left behind or struggle to keep up with the latest developments.
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