Multi-Edge Collaborative Caching with Robust Federated Learning
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Solution Overview
Problem
Existing Mobile Edge Computing (MEC) systems face challenges in efficient multi-edge collaboration, irrational cache resource configuration, and model robustness due to unintentional model corruption and adversarial attacks.
Innovation Solution
The proposed Collaborative Caching Framework for Multi-edge Systems with Robust Federated Deep Learning (RFDL) utilizes multi-dimensional cache space partitioning, Vector-Quantized Variational Auto-Encoder (VQ-VAE) for content popularity prediction, and a proactive cache replacement strategy with residual-based detection and similarity-based federated aggregation to enhance cache performance and model robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional centralized machine learning framework is used, then model scalability is limited, but computation and communication overheads increase with increasing number of end devices
Solution Approach 1:
The patent divides the centralized machine learning system into multiple distributed MEC nodes that independently perform local model training. Each MEC node processes data locally and only exchanges model parameters with the server, segmenting the computational workload and reducing communication overhead while improving scalability to handle increasing numbers of end devices.
2Productivity
If Federated Learning is used for multi-edge collaborative caching, then cache resource configuration is optimized, but model robustness degrades due to unintentional model corruption and adversarial attacks
Solution Approach 1:
The patent implements preliminary detection mechanisms before model aggregation. The server detects adversarial models and corrupt data by analyzing model updates from MEC nodes before incorporating them into the global model. This preliminary action prevents harmful updates from degrading model robustness while maintaining the benefits of federated learning for cache optimization.
Solution Approach 2:
The patent converts the presence of adversarial attacks and model corruption into a benefit by using them as training signals for detection mechanisms. The system learns to identify and filter harmful updates, making the federated learning process more robust. The adversarial examples ultimately improve the system's ability to distinguish between legitimate and malicious model updates.
3Loss of time
If cache space is increased to improve cache performance, then access delay is reduced, but system costs increase
Solution Approach 1:
The patent merges cache resources across multiple MEC nodes through collaborative caching. Instead of each node requiring large individual cache spaces, the system combines cache capacities distributed across the network, achieving the same overall caching effect with more efficient resource utilization and reduced total system costs while maintaining low access delay.
Data Source
AI summary
A Collaborative Caching Framework for Multi-edge Systems with Robust Federated Deep Learning is provided. First, we design a new partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a VQ-VAE-based accurate prediction for content popularity by overcoming posterior collapse. Finally, we create a new training mode and proactive cache replacement strategy based on robust federated deep learning. Specifically, residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using real-world testbed and MovieLens datasets, extensive experiments verify that RoCoCache achieves higher cache hit rates and efficiency than benchmark methods while ensuring better robustness. Moreover, we demonstrate the effectiveness of the components designed in RoCoCache via ablation experiments.