Federated Learning Model Aggregation for Connected Vehicles
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Solution Overview
Problem
Current federated learning systems require numerous communication rounds to achieve optimal weights for machine learning models in connected vehicle applications, due to varying data distributions across users and communication bandwidth constraints, which slows down the training process and raises privacy concerns.
Innovation Solution
A system that uses a server to obtain and adjust weights for local gradients from vehicles based on contributions measured by angles between local and global gradients, employing game theory to determine weights and aggregate models efficiently, thereby reducing communication rounds and preserving user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional federated learning is used to train models in distributed manner, then user privacy is protected and raw data transmission is avoided, but the number of communication rounds increases and training convergence slows down
Solution Approach 1:
The system performs preliminary actions by pre-calculating contribution metrics and potential function values for each vehicle before the actual model aggregation. This allows the server to have ready-to-use weighting information that accelerates the aggregation process without requiring multiple iterative communication rounds, thus improving convergence speed while maintaining privacy protection
Solution Approach 2:
The system changes the parameter of weight determination from uniform or simple proportional weighting to contribution-based dynamic weighting. By introducing contribution metrics that measure each vehicle's actual impact on model improvement and using potential function comparisons, the system optimizes the aggregation process to converge faster with fewer communication rounds
2Measurement precision
If multiple communication rounds are conducted to obtain optimal weights, then model accuracy improves, but communication bandwidth consumption increases and time delay accumulates
Solution Approach 1:
The system implements feedback mechanisms where each vehicle's contribution to model improvement is measured and fed back to the server. The server uses this feedback to dynamically adjust aggregation weights, ensuring that vehicles making significant contributions receive higher weights. This feedback loop allows the system to achieve optimal model accuracy in fewer communication rounds by making informed weighting decisions based on actual performance impact
Solution Approach 2:
The system performs preliminary calculation of contribution metrics and potential function values before aggregation. This pre-computation of weighting information eliminates the need for multiple iterative rounds to determine appropriate weights, reducing total training time while maintaining the ability to achieve high model accuracy through optimized single-round or few-round aggregation
3Adaptability or versatility
If data distribution varies significantly across users, then personalized local model performance improves, but global model convergence becomes difficult and requires more communication rounds
Solution Approach 1:
The system applies local quality by allowing each vehicle to maintain its local data distribution characteristics while contributing to the global model. The contribution metric captures the unique value each local dataset provides, and the potential function comparison ensures that vehicles with different data distributions are appropriately weighted. This preserves local adaptability while achieving global convergence without requiring numerous communication rounds to harmonize diverse data distributions
Data Source
AI summary
A server for communication-efficient model aggregation in federated networks for connected vehicle applications is provided. The server includes a controller programmed to: obtain contributions of a plurality of vehicles in a federated learning framework; determine weights for local gradients received from the plurality of vehicles based on the contributions; adjust the weights based on a comparison of potential functions for the plurality of vehicles; and aggregate the local gradients based on the adjusted weights to obtain a global model.