LEADER 03669nam 22006615 450 001 9910616395503321 005 20251113202803.0 010 $a3-031-07838-1 024 7 $a10.1007/978-3-031-07838-5 035 $a(MiAaPQ)EBC7102098 035 $a(Au-PeEL)EBL7102098 035 $a(CKB)24950456900041 035 $a(PPN)264956990 035 $a(OCoLC)1346533648 035 $a(DE-He213)978-3-031-07838-5 035 $a(EXLCZ)9924950456900041 100 $a20220928d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFederated Learning Over Wireless Edge Networks /$fby Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (175 pages) 225 1 $aWireless Networks,$x2366-1445 311 08$aPrint version: Lim, Wei Yang Bryan Federated Learning over Wireless Edge Networks Cham : Springer International Publishing AG,c2022 9783031078378 320 $aIncludes bibliographical references and index. 327 $aFederated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works. 330 $aThis book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications. 410 0$aWireless Networks,$x2366-1445 606 $aTelecommunication 606 $aComputational intelligence 606 $aMachine learning 606 $aArtificial intelligence 606 $aCommunications Engineering, Networks 606 $aComputational Intelligence 606 $aMachine Learning 606 $aArtificial Intelligence 615 0$aTelecommunication. 615 0$aComputational intelligence. 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 14$aCommunications Engineering, Networks. 615 24$aComputational Intelligence. 615 24$aMachine Learning. 615 24$aArtificial Intelligence. 676 $a929.374 676 $a006.31 700 $aLim$b Wei Yang Bryan$01260190 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910616395503321 996 $aFederated learning over wireless edge networks$93033716 997 $aUNINA