Federated Learning Node Selection Conflict Resolution
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Solution Overview
Problem
There is no mechanism to detect and prevent/resolve conflicts in training node selection for federated learning across use cases/services when multiple FL aggregators are performing the selection on overlapping distributed nodes.
Innovation Solution
A method and apparatus for coordinating artificial intelligence or machine learning contributor selection in a network, which involves receiving conflict resolution requests from multiple network entities managing AI/ML trustworthiness, and transmitting updated candidate lists to resolve conflicts between FL distributed node candidate lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple FL aggregators independently select training nodes without coordination, then each aggregator can autonomously optimize for its specific use case, but conflicts arise when aggregators select the same distributed nodes, leading to resource exhaustion and selection failures
Solution Approach 1:
The patent introduces a coordinator entity that acts as an intermediary between multiple FL aggregators and distributed nodes. This coordinator receives candidate node lists from aggregators, detects conflicts where multiple aggregators target the same nodes, and resolves conflicts by selecting winners and excluding losers. This mediator approach allows each aggregator to maintain autonomous optimization while preventing resource exhaustion through centralized conflict management.
2Productivity
If training node selection is performed without trustworthiness assessment, then the selection process is simpler and faster, but the model accuracy and reliability deteriorate due to inclusion of untrustworthy nodes
Solution Approach 1:
The patent implements preliminary trustworthiness assessment of distributed nodes before they are selected for training. Each node is evaluated on multiple criteria including data quality, computational capability, and security posture. Only nodes passing this preliminary assessment are included in the candidate lists submitted to the coordinator. This preliminary filtering ensures that only reliable nodes participate in training, maintaining model accuracy while the automated assessment process keeps the selection efficient.
3Reliability
If comprehensive trustworthiness criteria are applied to node selection, then model reliability improves, but the complexity of the selection process increases
Solution Approach 1:
The patent segments the trustworthiness assessment into distinct evaluation dimensions: data quality assessment, computational capability evaluation, and security posture verification. Each dimension is handled by specialized modules that independently assess specific aspects. The coordinator then integrates these segmented assessments to make final selection decisions. This segmentation reduces overall complexity by breaking down the comprehensive trustworthiness evaluation into manageable, independent components.
Data Source
AI summary
There are provided measures for enhancement of training node selection for trustworthy federated learning. Such measures exemplarily comprise, at a first network entity coordinating artificial intelligence or machine learning contributor selection in a network, receiving, respectively from a first and a second of a plurality of second network entities managing artificial intelligence or machine learning trustworthiness in artificial intelligence or machine learning pipelines in said network, a first/second artificial intelligence or machine learning contributor selection conflict resolution request including a first/second federated learning distributed node candidate list including at least one first/second federated learning distributed node in said network, wherein each of said at least one first/second federated learning distributed node has trustworthiness capabilities satisfying first/second artificial intelligence or machine learning trustworthiness requirement criteria and is associated with a respective rank in said first/second federated learning distributed node candidate list, transmitting, respectively towards said first and said second of said plurality of second network entities, a first/second artificial intelligence or machine learning contributor selection conflict resolution response including an updated first/second federated learning distributed node candidate list removing a conflict between said first federated learning distributed node candidate list and said second federated learning distributed node candidate list.