Federated Learning Aggregation via Importance Weights
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Solution Overview
Problem
Federated learning for neural networks requires significant memory and computing resources, and existing methods do not effectively motivate clients to participate due to the lack of tailored neural networks that optimize their specific data contributions.
Innovation Solution
A system that aggregates neural network training information based on the relative contribution of each client's data to performance metrics, using techniques like weighted averaging and importance weighing to generate more accurate and relevant neural networks for clients, thereby enhancing their participation and model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented with traditional aggregation methods, then model training can be performed across multiple clients, but model accuracy and client motivation are insufficient due to lack of personalized optimization
Solution Approach 1:
The patent applies local quality by computing importance scores for each client's data based on their specific contribution to model performance. Each client receives a personalized aggregation weight reflecting their data's relative importance, enabling the global model to adapt to local data characteristics while maintaining overall federation benefits. This resolves the contradiction by making the aggregation process both globally coordinated and locally optimized.
Solution Approach 2:
The patent implements dynamics by making aggregation weights dynamic and adaptive rather than static. Importance scores are computed iteratively based on current model performance metrics, allowing the system to adapt aggregation strategies as training progresses and as different clients contribute varying levels of value. This dynamic approach improves model accuracy while accommodating diverse client contributions.
2Adaptability or versatility
If more clients are motivated to join federated learning through personalized models, then system versatility improves, but memory and computing resource consumption increases
Solution Approach 1:
The patent applies self-service by enabling each client to independently compute their own importance score based on their local data and model performance. Clients autonomously determine their contribution value without requiring centralized computation of all client weights, reducing server-side computing burden while maintaining personalized optimization. This resolves the resource consumption issue while preserving client-specific adaptation.
Solution Approach 2:
The patent changes parameters by computing importance scores based on performance metrics rather than using fixed aggregation weights. This parameter transformation allows the system to adapt to varying client contributions dynamically, motivating more clients to join with personalized treatment while managing computational resources efficiently through metric-based rather than brute-force methods.
3Reliability
If traditional federated aggregation is used, then computing resources are consumed uniformly across all clients, but model accuracy suffers due to lack of importance-based weighting
Solution Approach 1:
The patent implements feedback by using model performance metrics to compute importance scores that guide aggregation. The system continuously monitors how each client's data affects model performance and adjusts aggregation weights accordingly. This feedback loop ensures that clients contributing more valuable data receive higher weights, improving model accuracy while maintaining training efficiency through informed rather than uniform resource allocation.
Solution Approach 2:
The patent changes the aggregation parameter from uniform weighting to importance-based weighting derived from performance metrics. This parameter transformation allows the system to prioritize clients whose data most improves model performance, resolving the contradiction between accuracy and efficiency by directing computational focus toward the most valuable contributions rather than treating all clients equally.
Data Source
AI summary
Apparatuses, systems, and techniques to train/use one or more neural networks. In at least one embodiment, a processor comprises one or more circuits to cause neural network training information to be aggregated based, at least in part, on contribution of the neural network training data and one or more performance metrics of the neural network.