Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

Core Concepts

A deep reinforcement learning-based vehicle selection scheme is proposed to enhance the safety and accuracy of asynchronous federated learning in vehicular edge computing by considering vehicle mobility, computational resources, data size, time-varying channel conditions, and Byzantine attacks.

Abstract

The paper presents a method for vehicle identification and selection for asynchronous federated learning (AFL) in vehicular edge computing (VEC) to improve the accuracy and safety of the global model. Key highlights: A deep reinforcement learning (DRL) based vehicle selection scheme is proposed to mitigate the influence of vehicles with limited local data, poor computational capabilities, poor communication channel status, and those affected by Byzantine attacks on the global model aggregation. The vehicle's mobility, time-varying channel status, and time-varying computational capabilities are considered in the AFL aggregation process to give more weight to vehicles with better performance, enhancing the global model's precision. A threshold-based mechanism is introduced to identify and exclude vehicles affected by Byzantine attacks before the AFL training, preventing the attacks from degrading the global model's accuracy. Simulation results demonstrate that the proposed scheme effectively improves the safety and accuracy of the global model compared to traditional AFL and federated learning approaches.

Stats

The local training delay of vehicle n is calculated as: T^n_l = D_n * C_0 / μ_n The transmission delay of uploading the local model of vehicle n at time t is calculated as: T^n_u(t) = |w| / R_n(t) The transmission rate of vehicle n at time t is calculated as: R_n(t) = B * log2(1 + p_0 * h_n(t) * (d_n(t))^(-α) / σ^2) The channel gain h_n(t) is modeled using an auto-regressive model: h_n(t) = ρ_n * h_n(t-1) + √(1-ρ_n^2) * e(t)

Quotes

"In vehicular networks with edge assistance, vehicle mobility has to be taken into account." "The variation in data quantity and computation capabilities among vehicles will impact the local training time in AFL and time-varying features of channels will also affect the model upload rate." "In AFL, vehicles' data may be attacked by Byzantine attacks. The prevention of Byzantine attacks is significantly important for AFL in VEC."

Key Insights Distilled From

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

by Cui Zhang,Xi... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08444.pdf

Deeper Inquiries

How can the proposed vehicle selection scheme be extended to handle more complex scenarios, such as dynamic vehicle arrivals and departures within the RSU coverage area

To extend the proposed vehicle selection scheme to handle dynamic vehicle arrivals and departures within the RSU coverage area, several adjustments can be made. One approach is to implement a real-time monitoring system that continuously updates the list of available vehicles within the coverage area. This system can track the movement of vehicles and dynamically adjust the selection process based on the current presence of vehicles. Additionally, incorporating predictive analytics can help anticipate the arrival or departure of vehicles, allowing the system to proactively adjust the selection criteria. By integrating these dynamic monitoring and predictive capabilities, the vehicle selection scheme can effectively adapt to changing scenarios in real-time.

What are the potential trade-offs between the accuracy improvement and the computational/communication overhead introduced by the DRL-based vehicle selection process

The potential trade-offs between accuracy improvement and computational/communication overhead in the DRL-based vehicle selection process need to be carefully considered. On one hand, the use of DRL can significantly enhance the accuracy of the global model by selecting vehicles with better performance and excluding those with poor performance or potential Byzantine attacks. This can lead to a more robust and precise system. However, the computational and communication overhead introduced by the DRL process may increase the complexity and resource requirements of the system. Balancing the need for accuracy improvement with the associated computational and communication costs is crucial. Optimization techniques, efficient algorithms, and resource management strategies can help mitigate these trade-offs and ensure a cost-effective and accurate vehicle selection process.

Can the proposed approach be generalized to other federated learning applications beyond vehicular edge computing, and what are the key considerations for adapting it to different domains

The proposed approach can be generalized to other federated learning applications beyond vehicular edge computing by adapting the key principles and considerations to different domains. Some key considerations for adapting the approach to other domains include: Data Characteristics: Understanding the specific data characteristics and requirements of the new domain is essential. Adapting the vehicle selection scheme to accommodate the unique data structures and features of the new application is crucial for its effectiveness. System Dynamics: Considering the system dynamics and operational requirements of the new domain is important. Adapting the vehicle selection process to align with the specific dynamics and constraints of the new application will ensure optimal performance. Security and Privacy: Addressing security and privacy concerns specific to the new domain is vital. Implementing measures to protect data integrity and confidentiality in the federated learning process is essential for maintaining trust and compliance. Scalability and Resource Management: Ensuring scalability and efficient resource management in the federated learning process is key. Adapting the approach to handle varying scales of data and resource availability in different domains will optimize performance and reliability. By carefully considering these factors and tailoring the approach to suit the requirements of the specific domain, the proposed vehicle selection scheme can be successfully generalized to a wide range of federated learning applications.