19.2 Federated learning and privacy-preserving deep learning
2 min read•july 25, 2024
enables collaborative model training without sharing raw data, preserving privacy. It's driven by increasing data protection concerns and regulations, allowing organizations to learn from distributed datasets while keeping sensitive information local.
Key principles include local updates, parameter aggregation, and . Challenges involve , with , and balancing privacy with utility. Techniques like and enhance data protection.
Federated Learning Fundamentals
Principles of federated learning
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Federated Learning enables decentralized machine learning on distributed datasets while preserving data privacy by keeping data locally
Motivation stems from increasing data privacy concerns, regulatory requirements (GDPR), and need for collaborative learning without data sharing
protects sensitive information during model training ensuring confidentiality of individual data points
Key Principles involve , aggregation of model parameters, and iterative learning process
Implementation of federated algorithms
Deep Learning Frameworks for Federated Learning include (TFF), , and
Simulation Steps involve data partitioning, local model training, model parameter aggregation, and global model update
Federated Averaging (FedAvg) Algorithm encompasses , local training, , and server update
Communication Protocols utilize and to enhance efficiency
Privacy techniques in deep learning
Differential Privacy (DP) defined by ϵ-differential privacy adds noise to protect individual data points
include Laplace and Gaussian mechanisms requiring careful privacy budget management
Secure Multi-Party Computation (SMPC) employs , , and
Integration with Deep Learning leverages , secure aggregation protocols, and encrypted inference
Challenges of federated learning
Communication Efficiency addresses bandwidth limitations through compression techniques (, )
Model Convergence tackles non-IID data, stragglers, dropped clients, and adaptive learning rates
manages concept drift, balances personalization vs global model performance, and considers fairness
balances privacy mechanisms with model accuracy and performance
handles varying computational capabilities, device availability, and reliability of clients
focuses on managing large numbers of clients and implementing asynchronous updates