Edge-Client Federated Graph Learning with Adaptive Neighbor Generation
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Solution Overview
Problem
Existing Federated Graph Learning (FGL) approaches overlook the potential links between subgraphs of clients, leading to insufficient feature propagation and degraded performance in classification tasks, while also suffering from high training costs and single-point overload issues as the number of clients expands.
Innovation Solution
The proposed Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation framework, named FedGL and its extension SpreadFGL, addresses these challenges by leveraging an edge server as an intermediary to facilitate information flow among clients, incorporating a graph imputation generator with a versatile assessor and negative sampling mechanism to generate learnable links, and extending the framework to a multi-edge environment for efficient training and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If FGL approaches use distributed subgraphs for training, then data privacy is protected, but potential links between clients are overlooked leading to insufficient feature propagation
Solution Approach 1:
The patent introduces an edge server as an intermediary component that mediates between multiple clients. The edge server aggregates graph data from multiple clients, performs global graph construction and link prediction, then returns augmented subgraphs to clients. This intermediary structure enables clients to benefit from global graph information without directly sharing their private data, thus resolving the contradiction between privacy protection and information completeness.
2Adaptability or versatility
If the number of clients expands in FGL, then more data diversity is achieved, but training costs increase and single-point overload occurs
Solution Approach 1:
The patent segments the training process into two distinct phases: a global training phase on the edge server that processes aggregated graph data from multiple clients, and a local training phase on individual clients that processes augmented subgraphs. This segmentation allows the system to leverage data diversity from multiple clients while distributing the computational burden, preventing single-point overload and maintaining training efficiency as the number of clients expands.
3Reliability
If link prediction is performed using only local client information, then client privacy is maintained, but cross-client information cannot be recovered
Solution Approach 1:
The edge server acts as a privacy-preserving intermediary that enables cross-client information recovery. It aggregates graph data from multiple clients to perform global link prediction, identifying potential links that span across different clients' subgraphs. The edge server then returns these predicted links to the respective clients as augmented data, allowing each client to improve its local model with cross-client information without other clients' raw data ever leaving their premises.
Data Source
AI summary
An Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation is provided. To promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL.