Federated Learning Systems: Towards Next-Generation AI
Muhammad Habib ur Rehman · Mohamed Medhat Gaber
2021년 6월 · Springer Nature
eBook
196
페이지
report검증되지 않은 평점과 리뷰입니다. 자세히 알아보기
eBook 정보
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.
이 eBook 평가
의견을 알려주세요.
읽기 정보
스마트폰 및 태블릿
노트북 및 컴퓨터
컴퓨터의 웹브라우저를 사용하여 Google Play에서 구매한 오디오북을 들을 수 있습니다.
eReader 및 기타 기기
Kobo eReader 등의 eBook 리더기에서 읽으려면 파일을 다운로드하여 기기로 전송해야 합니다. 지원되는 eBook 리더기로 파일을 전송하려면 고객센터에서 자세한 안내를 따르세요.
