001449985 000__ 03352cam\a2200565\i\4500
001449985 001__ 1449985
001449985 003__ OCoLC
001449985 005__ 20230310004502.0
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001449985 008__ 221005s2022\\\\sz\\\\\\ob\\\\001\0\eng\d
001449985 019__ $$a1347024245
001449985 020__ $$a9783031078385$$q(electronic bk.)
001449985 020__ $$a3031078381$$q(electronic bk.)
001449985 020__ $$z3031078373
001449985 020__ $$z9783031078378
001449985 0247_ $$a10.1007/978-3-031-07838-5$$2doi
001449985 035__ $$aSP(OCoLC)1346533648
001449985 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGZM$$dGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ$$dN$T
001449985 049__ $$aISEA
001449985 050_4 $$aQ325.5
001449985 08204 $$a006.3/1$$223/eng/20221013
001449985 1001_ $$aLim, Wei Yang Bryan,$$eauthor.
001449985 24510 $$aFederated learning over wireless edge networks /$$cWei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao.
001449985 264_1 $$aCham :$$bSpringer,$$c2022.
001449985 300__ $$a1 online resource.
001449985 336__ $$atext$$btxt$$2rdacontent
001449985 337__ $$acomputer$$bc$$2rdamedia
001449985 338__ $$aonline resource$$bcr$$2rdacarrier
001449985 4901_ $$aWireless networks
001449985 504__ $$aIncludes bibliographical references and index.
001449985 5050_ $$aFederated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works.
001449985 506__ $$aAccess limited to authorized users.
001449985 520__ $$aThis book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications.
001449985 650_0 $$aMachine learning.
001449985 650_0 $$aWireless communication systems.
001449985 650_0 $$aEdge computing.
001449985 655_0 $$aElectronic books.
001449985 7001_ $$aNg, Jer Shyuan,$$eauthor.
001449985 7001_ $$aXiong, Zehui,$$eauthor.
001449985 7001_ $$aNiyato, Dusit,$$eauthor.
001449985 7001_ $$aMiao, Chunyan,$$eauthor.
001449985 77608 $$iPrint version:$$z3031078373$$z9783031078378$$w(OCoLC)1322046989
001449985 830_0 $$aWireless networks (Springer (Firm))
001449985 852__ $$bebk
001449985 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-07838-5$$zOnline Access$$91397441.1
001449985 909CO $$ooai:library.usi.edu:1449985$$pGLOBAL_SET
001449985 980__ $$aBIB
001449985 980__ $$aEBOOK
001449985 982__ $$aEbook
001449985 983__ $$aOnline
001449985 994__ $$a92$$bISE