Technology Category
Machine Learning: Distributed Machine Learning & Federated LearningCognitive Modeling & Cognitive Systems: Agent ArchitecturesMultiagent Systems: Agent/AI Theories and Architectures
Principles and Components of Federated Learning Architectures
🤖 AI Summary
Federated learning (FL) faces systemic challenges across five dimensions—communication infrastructure, model design, data partitioning, privacy preservation, and system heterogeneity—yet lacks a holistic architectural framework to guide systematic design. Method: We propose the first full-stack FL architecture taxonomy, derived through rigorous architectural analysis, comprehensive literature review, and pattern abstraction. Our framework explicitly characterizes inter-module coordination mechanisms and fundamental trade-offs, and integrates key techniques—including differential privacy, secure aggregation, and heterogeneity-aware adaptation—into standardized, reusable architectural patterns. Contribution/Results: This work establishes the first systematic, end-to-end FL architecture guideline, bridging a critical gap in FL systems engineering. It provides theoretically grounded yet practically deployable architectural blueprints for privacy-sensitive distributed modeling, enabling principled design of scalable, robust, and privacy-preserving FL systems.
Technology Category
Application Category
📝 Abstract
Federated learning, also known as FL, is a machine learning framework in which a significant amount of clients (such as mobile devices or whole enterprises) collaborate to collaboratively train a model while keeping decentralized training data, all overseen by a central server (such as a service provider). There are advantages in terms of privacy, security, regulations, and economy with this decentralized approach to model training. FL is not impervious to the flaws that plague conventional machine learning models, despite its seeming promise. This study offers a thorough analysis of the fundamental ideas and elements of federated learning architectures, emphasizing five important areas: communication architectures, machine learning models, data partitioning, privacy methods, and system heterogeneity. We additionally address the difficulties and potential paths for future study in the area. Furthermore, based on a comprehensive review of the literature, we present a collection of architectural patterns for federated learning systems. This analysis will help to understand the basic of Federated learning, the primary components of FL, and also about several architectural details.
Problem
Research questions and friction points this paper is trying to address.
Analyzes federated learning communication architectures
Explores privacy methods in decentralized data
Addresses system heterogeneity in model training
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decentralized training data management
Central server oversight
Comprehensive FL architecture analysis
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