FedFed: Feature Distillation against Data Heterogeneity in Federated Learning (2310.05077v1)

Abstract: Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting model performance. To alleviate the dilemma, we raise a fundamental question: \textit{Is it possible to share partial features in the data to tackle data heterogeneity?} In this work, we give an affirmative answer to this question by proposing a novel approach called {\textbf{Fed}erated \textbf{Fe}ature \textbf{d}istillation} (FedFed). Specifically, FedFed partitions data into performance-sensitive features (i.e., greatly contributing to model performance) and performance-robust features (i.e., limitedly contributing to model performance). The performance-sensitive features are globally shared to mitigate data heterogeneity, while the performance-robust features are kept locally. FedFed enables clients to train models over local and shared data. Comprehensive experiments demonstrate the efficacy of FedFed in promoting model performance.

An Analysis of "FedFed: Feature Distillation against Data Heterogeneity in Federated Learning"

The paper "FedFed: Feature Distillation against Data Heterogeneity in Federated Learning" presents a novel approach to address pivotal challenges in Federated Learning (FL), namely data heterogeneity and privacy preservation. Federated Learning, while a promising technique for decentralized model training without data sharing, faces significant challenges due to non-IID (Non-Independent and Identically Distributed) data across various clients, which can hamper model performance stability and convergence rates. This paper proposes a method called Federated Feature Distillation (FedFed) to navigate the balance between performance enhancement and privacy assurance in the context of data heterogeneity.

Core Proposition and Methodology

The FedFed framework divides data features into two distinct categories: performance-sensitive features and performance-robust features. Performance-sensitive features are hypothesized to greatly contribute to overall model generalization and are shared among clients. Meanwhile, performance-robust features contribute less to model effectiveness and are kept private. This distillation approach draws from the principles of the Information Bottleneck (IB) method, which seeks to extract minimal sufficient information for model generalization.

A noteworthy technique adopted in FedFed is the injection of random noise to performance-sensitive features before sharing, a strategy steeped in differential privacy (DP) principles. By doing so, the framework attempts to mitigate privacy risks while maintaining model performance. Gaussian noise addition ensures that the shared features comply with differential privacy standards, ensuring a balance between privacy and the utility of shared information.

Empirical Evaluation

The approach was tested across several standard FL algorithms, including FedAvg, FedProx, SCAFFOLD, and FedNova, demonstrating performance benefits in diverse federated settings. Notably, across varying degrees of data heterogeneity, FedFed consistently improved convergence rates and model accuracy. For example, the technique yielded a notable performance gain of up to 40.67% in particular CIFAR-10 scenarios while also enhancing convergence times significantly compared to baseline algorithms without FedFed.

Additionally, the integration of FedFed showed reduced communication rounds needed to attain target accuracy and elevated model accuracy, offering evidence for the practical benefits of the proposed feature partitioning strategy. However, results on datasets like SVHN and FMNIST, where existing methods nearly match centralized performance, indicate that FedFed's potential might be contingent on the heterogeneity degree.

Technical Implications and Future Directions

The ability of FedFed to decouple the data into minimal sets necessary for training introduces a new technique in balancing privacy with performance, especially in non-IID federated settings. By enabling individual clients to augment their local data with performance-sensitive features shared across the network, FedFed underscores a creative use of differential privacy to foster robust, privacy-preserving distributed learning.

From a theoretical perspective, while the application of the IB framework in dividing feature sets is innovative, it does introduce computational overheads related to feature extraction and protection processes. Future research could explore optimized algorithms that further minimize overhead while maintaining model accuracy and privacy.

Additionally, FedFed's reliance on shared performance-sensitive features presents opportunities for exploring its applications in other domains, such as real-time recommendation systems or medical image analysis, as mentioned in the paper. Further work could also investigate extending FedFed to accommodate more sophisticated cryptographic techniques to bolster privacy protections and handle more pronounced privacy regulations.

In conclusion, "FedFed" represents a substantive contribution to federated learning research by methodically addressing inherent challenges of data heterogeneity with privacy-preserving methods. As federated learning becomes increasingly pertinent with the rise of edge and mobile computing, frameworks like FedFed could provide foundational methodologies steering future advancements in this domain.