LEADER 04596nam 22007335 450 001 9910986132203321 005 20250306115245.0 010 $a9789819632121 010 $a9819632129 024 7 $a10.1007/978-981-96-3212-1 035 $a(MiAaPQ)EBC31946520 035 $a(Au-PeEL)EBL31946520 035 $a(CKB)37783643500041 035 $a(DE-He213)978-981-96-3212-1 035 $a(OCoLC)1505029378 035 $a(EXLCZ)9937783643500041 100 $a20250306d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCross-device Federated Recommendation $ePrivacy-Preserving Personalization /$fby Xiangjie Kong, Lingyun Wang, Mengmeng Wang, Guojiang Shen 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (187 pages) 225 1 $aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 311 08$a9789819632114 311 08$a9819632110 327 $aChapter 1. Introduction -- Chapter 2. Learning Paradigms in Cross-device Federated Recommendation -- Chapter 3. Privacy Computing in Cross-device Federated Recommendation -- Chapter 4. Federated Issues in Cross-device Federated Recommendation -- Chapter 5. Future Prospects. 330 $aThis book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed computing paradigm, cross-device federated learning enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant. This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point. This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence. 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aData mining 606 $aData protection$xLaw and legislation 606 $aMachine learning 606 $aArtificial intelligence 606 $aExpert systems (Computer science) 606 $aData Mining and Knowledge Discovery 606 $aPrivacy 606 $aMachine Learning 606 $aIntelligence Infrastructure 606 $aKnowledge Based Systems 615 0$aData mining. 615 0$aData protection$xLaw and legislation. 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aExpert systems (Computer science) 615 14$aData Mining and Knowledge Discovery. 615 24$aPrivacy. 615 24$aMachine Learning. 615 24$aIntelligence Infrastructure. 615 24$aKnowledge Based Systems. 676 $a006.312 700 $aKong$b Xiangjie$01790676 701 $aWang$b Lingyun$01790677 701 $aWang$b Mengmeng$01790678 701 $aShen$b Guojiang$01790679 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910986132203321 996 $aCross-Device Federated Recommendation$94327407 997 $aUNINA