Clustered Federated Learning Signaling to Reduce Network Bottlenecks
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Solution Overview
Problem
Conventional federated learning architectures rely on a centralized server, leading to bottlenecks, significant communication overhead, challenges due to computational heterogeneity, and security and privacy concerns.
Innovation Solution
A clustered or hierarchical approach to federated learning, where network nodes are grouped into clusters led by designated leaders, with cluster formation and leader selection network-assisted, allowing for distributed neural network training and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized server architecture is used for federated learning, then coordination and model aggregation can be simplified, but communication overhead increases significantly and bottlenecks occur
Solution Approach 1:
The patent divides the centralized federated learning system into multiple hierarchical levels: edge servers manage local clusters of user equipment, while a central parameter server coordinates between edge servers. This segmentation reduces communication overhead by localizing model aggregation at edge servers, preventing all devices from communicating directly with the central server simultaneously.
Solution Approach 2:
Edge servers act as intermediaries between user equipment and the central parameter server. They perform local model aggregation and preprocessing, reducing the volume of data that needs to be transmitted to the central server and distributing the coordination burden away from a single centralized point.
2Device complexity
If a centralized server architecture is used for federated learning, then model aggregation can be centralized, but bottlenecks and single points of failure occur
Solution Approach 1:
The patent creates a hierarchical structure with multiple edge servers distributed across different locations, each managing its own cluster of devices. This segmentation eliminates the single point of failure problem by distributing the aggregation function across multiple independent nodes rather than relying on a single centralized server.
Solution Approach 2:
The system dynamically selects which edge servers participate in global model aggregation based on their current status, capacity, and proximity to devices. This dynamic allocation provides fault tolerance, as the system can adapt if certain edge servers become unavailable or overloaded.
3Extent of automation
If all devices train models and send updates to a central server, then learning can be distributed, but communication overhead becomes significant
Solution Approach 1:
The patent organizes devices into local clusters managed by edge servers. Each edge server performs local model aggregation with its cluster members, significantly reducing the number of individual device-to-server communications. Only the aggregated edge server models need to be transmitted to the central parameter server, exponentially reducing total communication overhead.
Solution Approach 2:
The patent combines multiple device model updates at the edge server level before transmitting to the central server. By merging updates locally, the system reduces redundant information transmission and minimizes the total volume of data that must traverse the network to reach the central parameter server.
Data Source
AI summary
Signaling procedures are provided which allow UEs to be formed into clusters via network assistance and perform ML model training in a clustered FL environment. A UE provides a first message including FL information to a network node. The UE also provides a second message indicating the network node. The UE obtains a third message indicating this network node or a different network node as an FL cluster leader and indicating an FL cluster of the UE based on the FL information. As a result, ML model training may be achieved in a distributed manner using clustered FL with minimization or avoidance of bottlenecks, communication overhead, challenges to model training due to heterogeneity of computational resources, training data, training tasks, or associated ML models, security and privacy challenges, or other limitations associated with conventional FL.