19.2 Federated learning and privacy-preserving deep learning

Federated learning 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 iterative learning. Challenges involve communication efficiency, model convergence with non-IID data, and balancing privacy with utility. Techniques like differential privacy and secure multi-party computation 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
• Privacy-Preserving Deep Learning protects sensitive information during model training ensuring confidentiality of individual data points
• Key Principles involve local model updates, aggregation of model parameters, and iterative learning process

Implementation of federated algorithms

• Deep Learning Frameworks for Federated Learning include TensorFlow Federated (TFF), PySyft, and FATE
• Simulation Steps involve data partitioning, local model training, model parameter aggregation, and global model update
• Federated Averaging (FedAvg) Algorithm encompasses client selection, local training, weight averaging, and server update
• Communication Protocols utilize secure aggregation and compression techniques to enhance efficiency

Privacy techniques in deep learning

• Differential Privacy (DP) defined by $\epsilon$-differential privacy adds noise to protect individual data points
• Noise addition mechanisms include Laplace and Gaussian mechanisms requiring careful privacy budget management
• Secure Multi-Party Computation (SMPC) employs Yao's Garbled Circuits, Secret Sharing, and Homomorphic Encryption
• Integration with Deep Learning leverages DP-SGD, secure aggregation protocols, and encrypted inference

Challenges of federated learning

• Communication Efficiency addresses bandwidth limitations through compression techniques (gradient quantization, sparse updates)
• Model Convergence tackles non-IID data, stragglers, dropped clients, and adaptive learning rates
• Data Heterogeneity manages concept drift, balances personalization vs global model performance, and considers fairness
• Privacy-Utility Trade-off balances privacy mechanisms with model accuracy and performance
• System Heterogeneity handles varying computational capabilities, device availability, and reliability of clients
• Scalability focuses on managing large numbers of clients and implementing asynchronous updates