Client Selection for Federated Learning in Vehicular Edge Computing: A Deep Reinforcement Learning Approach
ABSTARCT :
Vehicular edge computing (VEC) has emerged as a solution that places computing resources at the edge of the network to address resource management, service continuity, and scalability issues in dynamic vehicular environments. However, VEC faces challenges such as task offloading, varying communication conditions, and data security.
To tackle these challenges, federated learning (FL), a distributed machine learning framework that allows multiple clients to collaboratively train a global model without sharing their data, is utilized. However, vehicular clients have characteristics such as non-independent and identically distributed (non-IID) data, diverse communication capabilities, and high mobility, which pose difficulties for model convergence.
A dynamic and optimal client selection method is required to address VEC and FL challenges. Therefore, in this paper, we propose a distributed client selection method with multi-objectives that can dynamically adapt to changing conditions.
EXISTING SYSTEM :
To improve the accuracy and efficiency of model aggregation, this paper proposes a selective model aggregation approach. First of all, we exploit a geometric model that illustrates the relationship between the object of interest and the camera in each vehicular client.
The geometric model is used to evaluate the image quality in the motion blur level by observing the instantaneous velocity of each vehicular client. After that, the computation capability is quantified via a parameter of resource consumption.
By evaluating local image quality as well as computation capability, the ‘‘fine’’ local DNN models on the ‘‘fine’’ clients are selected and sent to the central server for aggregation. Since federated learning prevents from sending local data, the central server is not aware of the image quality and computation capability of vehicular clients, which is called information asymmetry.
To deal with the information asymmetry, the selection procedure of the ‘‘fine’’ local DNN models is formulated as a two-dimensional image-computation-reward contract theory problem.
DISADVANTAGE :
DRL Complexity: Deep reinforcement learning models can be computationally intensive, especially when dealing with a large number of clients in a vehicular network. Training DRL agents typically requires significant computational resources, which may not always be feasible in resource-constrained edge devices.
Large Client Base: Vehicular networks can involve a large number of vehicles (clients) spread over a wide area. The DRL-based client selection mechanism may struggle to scale efficiently with increasing numbers of clients, leading to high communication and computational costs.
Vehicle Mobility: The mobility of vehicles in a dynamic environment can introduce uncertainties in client availability and network stability, making the DRL-based client selection less reliable over time.
PROPOSED SYSTEM :
In this system, vehicles, acting as clients, collaboratively train a global machine learning model without sharing their local data, ensuring privacy preservation.
However, the highly dynamic nature of vehicular networks, characterized by vehicle mobility, fluctuating network conditions, and varying client resource capabilities, poses significant challenges for effective client selection.
To address these challenges, the proposed system employs a DRL agent that learns to make real-time, adaptive decisions on which vehicles should participate in each round of federated learning.
The DRL agent is trained to consider various factors such as the vehicle's computational resources (CPU, memory), battery level, data quality, connectivity status, and geographical location.
The system continuously evaluates the environment and adjusts its client selection strategy to optimize the performance of the federated learning process.
ADVANTAGE :
Context-Aware Decisions: DRL models can take into account various factors such as the vehicle’s location, connectivity status, available computational resources, and network latency. This enables more context-aware client selection, improving the quality of federated learning by choosing the best clients for the current situation.
Reduced Communication Overhead: By selecting clients that are well-suited for contributing to the model, DRL can help reduce the number of communication rounds and the amount of data transmitted, as fewer but more relevant clients are chosen. This is particularly important in vehicular edge networks where bandwidth and communication resources are limited.
Prevention of Malicious Clients: DRL can also be trained to recognize and avoid potentially malicious clients that may attempt to poison the model or inject faulty data. By incorporating security measures into the DRL model, it can make intelligent decisions to select trustworthy clients, improving the robustness of the system.
Fair Resource Distribution: DRL-based client selection can be designed to ensure fairness among clients by distributing the federated learning workload more evenly. For example, it can prevent certain vehicles with better resources (e.g., high-performance processors or stable connections) from dominating the learning process, giving smaller or more resource-constrained vehicles a chance to contribute as well. This leads to a more balanced system.
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