Federated Learning Collaboration Sets With Gradient-Based Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In current federated learning processes, data sets of some client network elements are not suitable for aggregation, leading to resource waste and reduced efficiency.
Innovation Solution
A method where network elements determine whether to join a collaboration set based on gradient information, using metrics such as gradient norms and data difference degrees to filter out inappropriate data sets, thereby optimizing resource utilization and reducing calculation and communication amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all client network elements participate in aggregation regardless of data suitability, then the number of participants is maximized, but resource waste increases and efficiency decreases
Solution Approach 1:
The patent applies preliminary action by having network elements calculate gradient norms and compare data difference degrees before joining the collaboration set. This pre-screening mechanism filters out unsuitable data sets in advance, preventing resource waste on inappropriate participants while maintaining the benefits of federated learning for suitable elements.
Solution Approach 2:
The patent changes the parameter of participation criteria from unconditional inclusion to conditional inclusion based on gradient norm thresholds and data difference degree comparisons. This parameter change enables the system to dynamically adjust which network elements participate in aggregation, optimizing resource utilization.
2Productivity
If all network elements participate in aggregation, then data coverage is maximized, but calculation amount and communication amount increase unnecessarily
Solution Approach 1:
The patent extracts and removes unsuitable network elements from the aggregation process by using gradient norm calculations and data difference degree comparisons to identify and exclude elements whose data sets are not suitable for aggregation. This extraction maintains the collaborative learning benefits while reducing unnecessary calculation and communication overhead.
3Ease of operation
If network elements join collaboration sets without gradient information comparison, then joining process is simplified, but resource utilization deteriorates
Solution Approach 1:
The patent applies self-service by enabling network elements to autonomously calculate their own gradient norms and compare data difference degrees without requiring centralized control. Each network element independently determines its suitability for joining the collaboration set, simplifying the overall process while optimizing resource utilization through decentralized decision-making.
Data Source
AI summary
A federated learning method includes receiving, by a first network element, first information from a second network element. The first information includes gradient information of a first collaboration set corresponding to the second network element, and the first collaboration set includes a collaboration network element configured to perform federated learning. The method further includes, determining, by the first network element, and based on the first information, to join the first collaboration set. The method further includes deciding, by the first network element, and based on the gradient information that is of the first collaboration set and that is delivered by the second network element, whether to join the first collaboration set to perform federated learning.