Federated Learning via Data-Free Knowledge Distillation
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Solution Overview
Problem
Current federated learning systems do not effectively address the heterogeneity of edge nodes in terms of local dataset size and computation resources, leading to high communication costs and inefficiencies in model training, especially in vehicular technologies where privacy and bandwidth constraints are significant.
Innovation Solution
The system employs data-free knowledge distillation, where edge nodes transmit abstracted knowledge rather than model parameters, allowing the server to generate aggregated knowledge that is used for local model training, reducing communication costs and enabling customization of model architecture without the need for centralized model aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional federated learning transmits model parameters between server and users, then model training can be performed in a distributed manner, but communication cost becomes remarkable especially in mobile edges
Solution Approach 1:
The patent extracts only the essential information (knowledge distillation outputs) from the full model parameters and transmits this compressed representation between clients and server. This extraction of critical information while discarding redundant data significantly reduces communication cost while maintaining the ability to perform distributed model training.
Solution Approach 2:
The patent uses knowledge distillation to create a compressed copy of model parameters that captures the essential learning patterns. This copy is transmitted instead of the full model parameters, enabling efficient communication while preserving the core training capability across distributed devices.
2Device complexity
If current federated learning is used without considering heterogeneous edge nodes, then implementation is simpler, but it cannot effectively address heterogeneity in local dataset size and computation resources
Solution Approach 1:
The patent implements local quality by allowing each edge node to perform knowledge distillation independently according to its own characteristics (dataset size, computation resources). Each node adapts the distillation process to its local conditions, enabling effective handling of heterogeneity while maintaining a relatively simple overall system architecture.
3Quantity of substance
If all raw data is transmitted to data center for centralized processing, then sufficient data is available for model training, but user privacy is compromised and communication bandwidth is consumed
Solution Approach 1:
The patent extracts only the essential learning patterns and knowledge from the raw data at local edge nodes through knowledge distillation. This extracted knowledge is transmitted to the server instead of raw data, preserving user privacy while maintaining sufficient information for centralized model training.
Solution Approach 2:
The patent segments the data processing task by performing feature extraction and knowledge distillation locally at each edge node before transmission. This segmentation keeps sensitive raw data at the edge while transmitting only processed knowledge, balancing data availability with privacy protection.
Data Source
AI summary
A system for training a model using federated learning is provided. The system includes a server, and a plurality of vehicles. Each of the vehicles includes a controller programmed to: transmit first knowledge data including information about a plurality of feature vectors and information about a plurality of predictions, receive first aggregated knowledge from the server, and train a local model based on the first aggregated knowledge. The server averages the first knowledge data received from the plurality of vehicles to generate the aggregated knowledge.