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Differentially Private Federated Learning with Adaptive Local Iterations

Core Concepts
ALI-DPFL algorithm optimizes federated learning by dynamically adjusting local iterations for improved performance under resource constraints.
The content discusses the ALI-DPFL algorithm, focusing on differential privacy in federated learning. It analyzes convergence, introduces adaptive local iterations, and demonstrates superior performance through experiments on MNIST, FashionMNIST, and Cifar10 datasets. The algorithm outperforms existing schemes in resource-constrained scenarios. Structure: Introduction to Federated Learning and Privacy Concerns Existing Differential Privacy Techniques in Federated Learning Introduction of ALI-DPFL Algorithm with Adaptive Local Iterations Theoretical Convergence Analysis and Algorithm Workflow Privacy Analysis and Robustness Testing with Heterogeneous Data Comparative Experiments and Results
By theoretically analyzing the convergence, we can find the optimal number of local Differential Privacy Stochastic Gradient Descent (DPSGD) iterations for clients between any two sequential global updates. Existing DPFL schemes usually choose empirically a fixed number of local iterations τ for each global update. Through the convergence analysis, we derive a convergence bound related to the number τ of local iterations. We propose a differentially private federated learning scheme with adaptive local iterations (ALI-DPFL).
"As a result, Differential Privacy (DP) has been widely used in FL to prevent such attacks." "Many works have demonstrated that the technique of differential privacy (DP) could protect machine learning models from unintentional information leakage."

Key Insights Distilled From

by Xinpeng Ling... at 03-26-2024

Deeper Inquiries

How can adaptive local iterations improve model convergence in federated learning

Adaptive local iterations can improve model convergence in federated learning by dynamically adjusting the number of local iterations based on the current state of the model and data distribution. This adaptability allows the algorithm to optimize the convergence process, ensuring that each client contributes effectively to the global model update. By dynamically selecting the optimal number of local iterations, adaptive algorithms like ALI-DPFL can enhance convergence speed and accuracy, leading to better overall performance in resource-constrained scenarios.

What are the implications of overlooking communication round constraints in DPFL schemes

Overlooking communication round constraints in DPFL schemes can have significant implications on both privacy protection and model performance. When communication rounds are not properly constrained, it may lead to inefficient use of resources, increased privacy risks due to excessive information sharing between clients and servers, longer training times, and suboptimal convergence rates. Ignoring these constraints can result in models that are less robust against inference attacks and may compromise data privacy.

How does data heterogeneity impact the performance of privacy-preserving algorithms like ALI-DPFL

Data heterogeneity plays a crucial role in determining the performance of privacy-preserving algorithms like ALI-DPFL. In heterogeneous datasets where different clients possess varying distributions or characteristics of data samples, traditional federated learning approaches may struggle to achieve accurate global models due to differing contributions from each client. Algorithms like ALI-DPFL with adaptive strategies for handling diverse data types can mitigate these challenges by dynamically adjusting parameters such as sampling rates or noise levels based on individual client characteristics. This adaptability enables more efficient collaboration among clients with heterogeneous data distributions, ultimately improving model accuracy and convergence rates in federated learning settings characterized by data diversity.