Dinesh C. Verma

Federated AI for Real-World Business Scenarios

• Gebundenes Buch

Produktbeschreibung

This book provides a holistic overview of all aspects of federated AI, which allows creation of real-world applications in contexts where data is dispersed in many different locations.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 218
• Erscheinungstermin: 1. Oktober 2021
• Englisch
• Abmessung: 240mm x 161mm x 16mm
• Gewicht: 468g
• ISBN-13: 9780367861575
• ISBN-10: 0367861577
• Artikelnr.: 62223488

Autorenporträt

Dinesh C. Verma is an IBM Fellow, a UK Fellow of the Royal Academy of Engineering and an IEEE Fellow. He leads the Distributed AI area at IBM Watson Research Center. He has authored ten books, 150+ technical papers and been granted 185+ U.S. patents. He has led an international consortium of scientists for fifteen years, and supervised many business solutions using AI. More details about Dinesh are available at ibm.biz/dineshverma

Inhaltsangabe

1. Introduction to Artificial Intelligence. 2. Scenarios for Federated AI.
3. Naive Federated Learning Approaches. 4. Addressing Data Mismatch Issues
in Federated AI. 5. Addressing Data Skew Issues in Federated Learning. 6.
Addressing Trust Issues in Federated Learning. 7. Addressing
Synchronization Issues in Federated Learning. 8. Addressing Vertical
Partitioning Issues in Federated Learning. 9. Use Cases.

---

1. Introduction to Artificial Intelligence. 2. Scenarios for Federated AI. 3. Naive Federated Learning Approaches. 4. Addressing Data Mismatch Issues in Federated AI. 5. Addressing Data Skew Issues in Federated Learning. 6. Addressing Trust Issues in Federated Learning. 7. Addressing Synchronization Issues in Federated Learning. 8. Addressing Vertical Partitioning Issues in Federated Learning. 9. Use Cases.

Rezensionen

"Verma (IBM Watson Research Center) aims to explain federated AI from the perspective of the business analyst who is neither programmer nor statistician, yet faces real-world system requirements in planning an AI implementation. Verma defines the federated AI method as a way of determining business processes through AI models derived by software-driven analyses of pertinent data, where the analyzed data is siloed across disparate systems. He recommends a LEARN > INFER > ACT cycle to the practitioner and distinguishes between federated learning and federated inference, as the actual federation step may occur during either the LEARN or INFER modules of the cycle. Verma is thorough in describing the problem-solving issues which may arise when planning a federated AI implementation."

- M. Mounts, Dartmouth College, Choice, November 2022