Efficient Communication and Secure Federated Recommendation System via Low-rank Training

Conceitos essenciais

The author proposes the CoLR framework to reduce communication overhead in FedRec systems by leveraging low-rank structures, ensuring compatibility with secure aggregation protocols.

Resumo

The authors introduce the CoLR framework to address communication challenges in FedRec systems. By adjusting lightweight trainable parameters while keeping most parameters frozen, CoLR significantly reduces communication overhead without compromising performance. The method remains compatible with secure aggregation protocols, offering benefits such as reduced communication costs, low computational overheads, and adaptability to bandwidth heterogeneity.
In a centralized recommendation system, user privacy concerns have led to the emergence of federated recommendation (FedRec) systems. These systems involve transferring recommendation models between a central server and edge devices like mobile phones and laptops. Challenges include varying computational speeds and bandwidth capabilities among clients, leading to potential performance issues.
To reduce communication costs in practical FedRec systems, mechanisms like reducing communication frequency and message compression are commonly used. However, these methods may not align with secure aggregation protocols like Homomorphic Encryption (HE). The proposed CoLR framework addresses these challenges by enforcing a low-rank structure on item embedding matrix updates during training rounds.
Experimental results show that CoLR outperforms base models and compression methods like SVD and Top-K compression in terms of recommendation performance while significantly reducing communication costs. The method is also compatible with HE-based FedRec systems, offering privacy preservation benefits.
Overall, the CoLR framework presents a promising solution for efficient communication and secure federated recommendation systems by leveraging low-rank structures in model updates.

Estatísticas

Our approach substantially reduces communication overheads without introducing additional computational burdens.
The approach resulted in a reduction of up to 93.75% in payload size.
Code for reproducing our experiments can be found at https://github.com/NNHieu/CoLR-FedRec.
In experiments on the MovieLens-1M dataset, we adjust dimensions of user and item embeddings across different settings.
We set the number of clients participating in each round equal to 1% of all users in each dataset.

Citações

"Our approach substantially reduces communication overheads without introducing additional computational burdens."
"The method remains compatible with secure aggregation protocols."
"Experimental results show that CoLR outperforms base models and compression methods."

Principais Insights Extraídos De

Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training

by Ngoc-Hieu Ng... às arxiv.org 02-29-2024

https://arxiv.org/pdf/2401.03748.pdf

Perguntas Mais Profundas

How does the CoLR framework compare to other existing methods for reducing communication overhead

The CoLR framework offers several advantages over existing methods for reducing communication overhead in federated recommendation systems.

Efficiency: CoLR significantly reduces communication costs without introducing additional computational burdens. By leveraging the low-rank structure inherent in updating transfers, it optimizes the transmission of model updates between user devices and a central server.

Compatibility: Unlike some compression methods that may not align well with secure aggregation protocols like Homomorphic Encryption (HE), CoLR remains fully compatible with these protocols. This ensures that privacy is maintained while still achieving efficient communication.

Flexibility: CoLR allows for adaptive rank selection by clients based on their computational and communication capabilities, making it suitable for heterogeneous network environments where clients have varying resources.

Performance: In experiments comparing CoLR to other compression-based methods like SVD and Top-K compression, CoLR consistently demonstrated superior performance in terms of recommendation accuracy at similar or reduced communication costs.

Security: The compatibility of CoLR with HE enhances the security of federated recommendation systems by allowing encrypted operations on model updates without compromising data privacy.

What are the potential implications of implementing the CoLR framework on real-world federated recommendation systems

Implementing the CoLR framework in real-world federated recommendation systems could have significant implications:

Improved Privacy Protection: By reducing the amount of data transmitted between users' devices and a central server, CoLR helps safeguard sensitive user information from potential breaches during transmission.

Enhanced Communication Efficiency: The reduction in communication overhead achieved by using CoLR can lead to faster training times, lower bandwidth usage, and improved system performance overall.

Scalability and Adaptability: The flexibility offered by allowing clients to adjust their local rank based on available resources makes the system more adaptable to diverse network conditions and client capabilities.

Cost-Effectiveness: With reduced communication costs, organizations implementing federated recommendation systems can potentially save on infrastructure expenses related to data transfer and processing.

How can the concept of low-rank structures be further optimized for enhancing communication efficiency in FedRec systems

To further optimize low-rank structures for enhancing communication efficiency in FedRec systems, several strategies can be considered:

Dynamic Rank Adjustment: Implement algorithms that dynamically adjust the rank of updates based on factors such as network congestion levels or device capabilities during training rounds.

2 .Adaptive Compression Techniques: Develop adaptive compression techniques that intelligently select which parts of model updates should be compressed based on their importance or impact on recommendation performance.
3 .Hybrid Approaches: Explore hybrid approaches combining low-rank structures with other optimization techniques like quantization or sparsification to achieve even greater reductions in payload size while maintaining high recommendation accuracy.
4 .Distributed Computing Strategies: Utilize distributed computing strategies to distribute computation tasks efficiently across multiple nodes within a federated learning environment, optimizing both computation time and resource utilization.