Federated Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Core Concepts

FedSSA, a novel personalized heterogeneous federated learning framework, enhances the performance and efficiency of model-heterogeneous personalized federated learning through semantic similarity-based header parameter aggregation and adaptive parameter stabilization.

Abstract

The paper proposes the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks in model-heterogeneous personalized federated learning (MHPFL). FedSSA splits each client's local model into a heterogeneous feature extractor and a homogeneous classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation, where each client only uploads the parameters of its seen-class headers. The server aggregates these parameters by class to update the global header. For global-to-local knowledge transfer, FedSSA devises an adaptive parameter stabilization strategy that fuses the seen-class parameters of the client's historical local header and the latest global header to update the local header. This alleviates parameter shaking in the initial training rounds and speeds up convergence. Compared to existing MHPFL methods, FedSSA improves model performance, communication efficiency, and computation efficiency simultaneously. Theoretical analysis proves the convergence of FedSSA, and extensive experiments on two real-world datasets demonstrate its superiority over seven state-of-the-art MHPFL baselines.

Stats

The paper reports the following key metrics: FedSSA achieves up to 3.62% higher accuracy compared to the best baseline. FedSSA achieves 15.54 times higher communication efficiency compared to the best baseline. FedSSA achieves 15.52 times higher computation efficiency compared to the best baseline.

Quotes

"FedSSA consists of two core modules: 1) local-to-global knowledge transfer by semantic similarity-based header parameter aggregation and 2) global-to-local knowledge transfer by adaptive parameter stabilization-based header parameter fusion, both of them enhance the personalization of each client's heterogeneous local model and stabilize classification boundaries." "Extensive experiments demonstrate that FedSSA achieves the best classification accuracy while incurring the lowest communication and computational costs."

Key Insights Distilled From

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

by Liping Yi,Ha... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2312.09006.pdf

Deeper Inquiries

How can the semantic similarity-based aggregation rule be further improved to enhance the performance of FedSSA?

The semantic similarity-based aggregation rule in FedSSA can be further improved by incorporating more advanced techniques for measuring similarity between classification header parameters. One way to enhance this rule is to explore the use of more sophisticated similarity metrics, such as cosine similarity, Euclidean distance, or even neural network-based similarity measures. By utilizing more advanced similarity metrics, FedSSA can better capture the nuanced relationships between header parameters and improve the quality of the aggregation process. Additionally, incorporating adaptive weighting schemes based on the relevance or importance of each parameter could further enhance the aggregation rule. This would allow FedSSA to give more weight to parameters that are more critical for classification tasks, leading to more effective knowledge transfer and aggregation.

How can FedSSA be extended to other real-world applications beyond supervised classification tasks?

FedSSA can be extended to a variety of real-world applications beyond supervised classification tasks by adapting its framework to different machine learning problems. One potential extension is applying FedSSA to regression tasks, where the goal is to predict continuous values instead of discrete classes. By modifying the structure of the feature extractor and classification header to suit regression problems, FedSSA can be used for tasks such as predicting house prices, stock market trends, or medical diagnosis outcomes. Additionally, FedSSA can be applied to natural language processing tasks, such as sentiment analysis or text generation, by adjusting the model architecture and training process to accommodate text data. Furthermore, FedSSA can be utilized in anomaly detection, clustering, and reinforcement learning applications by customizing the model components and aggregation strategies to fit the specific requirements of these tasks.

What are the potential privacy implications of the FedSSA approach, and how can they be addressed?

The FedSSA approach, like any federated learning method, raises privacy concerns related to the transmission and aggregation of model parameters across multiple clients. One potential privacy implication is the risk of information leakage when clients share their local model parameters with the central server during the aggregation process. To address these privacy concerns, several measures can be implemented in FedSSA. Firstly, implementing secure and encrypted communication protocols between clients and the server can help protect the confidentiality of the transmitted data. By using techniques like secure multi-party computation and differential privacy, FedSSA can ensure that sensitive information is not exposed during the model aggregation process. Secondly, adopting federated learning techniques that focus on data anonymization and decentralized model training can further enhance privacy protection. By aggregating model updates in a privacy-preserving manner without directly accessing individual client data, FedSSA can mitigate the risk of privacy breaches. Moreover, incorporating robust authentication and access control mechanisms to verify the identities of clients and restrict unauthorized access to the federated learning system can strengthen the overall security and privacy of FedSSA. By implementing a comprehensive privacy framework that combines encryption, anonymization, and access control, FedSSA can address potential privacy implications and ensure the confidentiality of client data throughout the federated learning process.