Federated learning with Python : design and implement a federated learning system and develop applications using existing frameworks

Federated learning with Python design and implement a federated learning system and develop applications using existing frameworks

Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level Key Features Design distributed systems that can be applied to real-world federated learning applications at scale Discover multiple aggregation sc...

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Detalles Bibliográficos
Autor principal: Nakayama, Kiyoshi (-)
Otros Autores: Jeno, George
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing Limited 2022.
Materias:
Machine learning.
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009701323506719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Acknowledgments
  • Contributors
  • Table of Contents
  • Preface
  • Part 1 Federated Learning
  • Conceptual Foundations
  • Chapter 1: Challenges in Big Data and Traditional AI
  • Understanding the nature of big data
  • Definition of big data
  • Big data now
  • Triple-A mindset for big data
  • Data privacy as a bottleneck
  • Risks in handling private data
  • Increased data protection regulations
  • From privacy by design to data minimalism
  • Impacts of training data and model bias
  • Expensive training of big data
  • Model bias and training data
  • Model drift and performance degradation
  • How models can stop working
  • Continuous monitoring
  • the price of letting causation go
  • FL as the main solution for data problems
  • Summary
  • Further reading
  • Chapter 2: What Is Federated Learning?
  • Understanding the current state of ML
  • What is a model?
  • ML
  • automating the model creation process
  • Deep learning
  • Distributed learning nature
  • toward scalable AI
  • Distributed computing
  • Distributed ML
  • Edge inference
  • Edge training
  • Understanding FL
  • Defining FL
  • The FL process
  • FL system considerations
  • Security for FL systems
  • Decentralized FL and blockchain
  • Summary
  • Further reading
  • Chapter 3: Workings of the Federated Learning System
  • FL system architecture
  • Cluster aggregators
  • Distributed agents
  • Database servers
  • Intermediate servers for low computational agent devices
  • Understanding the FL system flow
  • from initialization to continuous operation
  • Initialization of the database, aggregator, and agent
  • Initial model upload process by initial agent
  • Overall FL cycle and process of the FL system
  • Synchronous and asynchronous FL
  • The aggregator-side FL cycle and process
  • The agent-side local retraining cycle and process
  • Model interpretation based on deviation from baseline outputs
  • Basics of model aggregation
  • What exactly does it mean to aggregate models?
  • FedAvg
  • Federated averaging
  • Furthering scalability with horizontal design
  • Horizontal design with semi-global model
  • Distributed database
  • Asynchronous agent participation in a multiple-aggregator scenario
  • Semi-global model synthesis
  • Summary
  • Further reading
  • Part 2 The Design and Implementation of the Federated Learning System
  • Chapter 4: Federated Learning Server Implementation with Python
  • Technical requirements
  • Main software components of the aggregator and database
  • Aggregator-side codes
  • lib/util codes
  • Database-side code
  • Toward the configuration of the aggregator
  • Implementing FL server-side functionalities
  • Importing libraries for the FL server
  • Defining the FL Server class
  • Initializing the FL server
  • Registration function of agents
  • The server for handling messages from local agents
  • The global model synthesis routine
  • Functions to send the global models to the agents

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