Handbook on Federated Learning
Venduto da Basi6 International, Irving, TX, U.S.A.
Venditore AbeBooks dal 24 giugno 2016
Nuovi - Brossura
Condizione: Nuovo
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Aggiungere al carrelloVenduto da Basi6 International, Irving, TX, U.S.A.
Venditore AbeBooks dal 24 giugno 2016
Condizione: Nuovo
Quantità: Più di 20 disponibili
Aggiungere al carrelloNew. US edition. Print on demand title. Delivery takes 20-25 days.
Codice articolo POD-459532
Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.
Saravanan Krishnan is working as Associate Professor at the Department of Computer Science & Engineering, College of Engineering, Guindy, Anna University, Tirunelveli, India. He has published papers in 14 international conferences and 30 reputed journals. He has also written 16 book chapters and nine books with reputed publishers. He is an active researcher and academician. Also, he is reviewer for many reputed journals published by Elsevier, IEEE etc.
A. Jose Anand is working as Professor at the Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, India. He has one year of industrial experience and twenty-four years of teaching experience. He has presented several papers at conferences. He has published several papers in reputed journals. He has also published books for polytechnic & engineering subjects. He is a Member of CSI, IEI, IET, IETE, ISTE, INS, QCFI and EWB. His current research interest is in Wireless Sensor Networks, Embedded Systems, IoT, Machine Learning and Image Processing, etc.
R. Srinivasan is working as Professor at the Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India having vast teaching experience. He received a Ph.D. in Computer Science and Engineering from Vel Tech University. His research interest spans across Computer Networking, Wireless Sensor Networks and Internet of Things (IoT). Much of his work has been on improvising the understanding, design and the performance of networked computer systems and performance evaluation. He is a recognised supervisor at Vel Tech University guiding 8 research scholars. He has published over 25 papers in reputed journals and conferences. He had delivered technical sessions to various reputed institutes. He has been a reviewer member for many conferences and has served as technical committee member. He is also a member in many professional societies and a member in IEEE. He has published several reputed articles. He is presently Editor in Chief for Wireless Networks, Peer-to-Peer Networking and Applications- Springer Series.
R. Kavitha received a master’s in software engineering from College of Engineering, Anna University, India and Ph. D in Computer Science and Engineering from Vel Tech, Chennai, India. Her research areas are Machine Learning, Image Processing and Software Engineering. She worked as Professor at Vel Tech, Chennai with 15 years of teaching experience. She had guided projects of many UG and PG students. She is a recognised supervisor at Vel Tech University guiding 8 research scholars. She has published over 35 papers in reputed journals. She is an active member of IEEE and IEEE WIE and has been a part of events in association with professional societies. She had delivered technical sessions to various reputed institutes. She has been a reviewer member for many conferences and has served as technical committee member.
S. Suresh was a Professor of Cloud Big Data and Analytics, Faculty of Computer Science and Engineering at P.A. College of Engineering and Technology, India. He undertook extensive research on Big Data & Analytics, Internet of Things and Machine Learning. He wrote more than 30 scientific papers some of which have been published in well-known journals from Elsevier, Springer, etc. and presented at important conferences. In his lifetime, he had received various best paper and best speaker awards. Suresh authored 6 books and numerous book chapters. He fetched research and events grants from various Indian agencies. His research is summarized at Google Scholar Citation. He also regularly tutors, advises and provides consulting support to regional firms with respect to their Cloud Big Data Analytics, IoT, Machine Learning and Mobile Application Development.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Visita la pagina della libreria
Professional Book Seller shipping from Multiple Locations Worldwide for fastest delivery possible!
Se sei un consumatore puoi recedere dal contratto in conformità con quanto segue. Per Consumatore si intende qualsiasi persona fisica che agisce per scopi estranei alla propria attività commerciale, imprenditoriale, artigianale o professionale.
Informazioni sul diritto di recesso
Diritto legale di recesso
Hai il diritto di recedere dal presente contratto entro 14 giorni senza fornire alcuna motivazione.
Il periodo di recesso scade dopo 14 giorni dal giorno in cui tu o una terza parte, diversa dal vettore e da te indicata, acquisisce il possesso fisico dell'ultimo bene o dell'ultimo lotto o pezzo.
Per esercitare il diritto di recesso, compila e invia elettronicamente una dichiarazione esplicita sul nostro sito Web, alla voce “I miei acquisti” nella sezione “Mio account”. Ti comunicheremo senza indugio una conferma di ricezione di tale recesso su un supporto durevole (ad es. via e-mail).
Per rispettare il termine di recesso, è sufficiente inviare la comunicazione relativa all'esercizio del diritto di recesso prima della scadenza del periodo di recesso stesso.
Effetti del recesso
In caso di recesso dal presente contratto, ti rimborseremo tutti i pagamenti ricevuti, compresi i costi di spedizione (ad eccezione dei costi supplementari derivanti dalla tua eventuale scelta di un tipo di spedizione diverso dal tipo meno costoso di consegna standard da noi offerto).
Potremo effettuare una detrazione dal rimborso per la perdita di valore dei beni forniti, qualora tale perdita sia il risultato di una manipolazione non necessaria da parte tua.
Eseguiremo il rimborso senza indebito ritardo e non oltre 14 giorni dal giorno in cui saremo informati della tua decisione di recedere dal presente contratto.
Il rimborso sarà effettuato utilizzando lo stesso mezzo di pagamento da te usato per la transazione iniziale, salvo che tu non abbia espressamente concordato altrimenti; in ogni caso, non dovrai sostenere alcun costo quale conseguenza di tale rimborso.
Possiamo trattenere il rimborso finché non avremo ricevuto i beni oppure finché non avrai fornito la prova di averli rispediti, a seconda di quale condizione si verifichi per prima.
Dovrai rispedire i beni o consegnarli a Basi6 International, Irving, Texas, U.S.A., senza indebito ritardo e, in ogni caso, entro 14 giorni dal giorno in cui ci hai comunicato la tua volontà di recedere dal presente contratto. Il termine è rispettato se rispedisci i beni prima della scadenza del periodo di 14 giorni. I costi diretti della restituzione dei beni saranno a tuo carico. Sei responsabile solo della diminuzione del valore dei beni risultante da una manipolazione diversa da quella necessaria per stabilire la natura, le caratteristiche e il funzionamento dei beni stessi.
Eccezioni al diritto di recesso
Il diritto di recesso non si applica a:
All orders shipped via FedEx or DHL and delivered to your doorstep within 3-5 days. We do not ship to P.O.Boxes and a proper street address must be provided to avoid any delays.
| Quantità dell'ordine | Da 5 a 14 giorni lavorativi | Da 3 a 6 giorni lavorativi |
|---|---|---|
| Primo articolo | EUR 0.00 | EUR 0.00 |
I tempi di consegna sono stabiliti dai venditori e variano in base al corriere e al paese. Gli ordini che devono attraversare una dogana possono subire ritardi e spetta agli acquirenti pagare eventuali tariffe o dazi associati. I venditori possono contattarti in merito ad addebiti aggiuntivi dovuti a eventuali maggiorazioni dei costi di spedizione dei tuoi articoli.