Federated Learning Coordinator Node for Privacy and Efficiency
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Solution Overview
Problem
Federated learning techniques face challenges such as node selection, data bias, fairness, efficient preprocessing, and understanding the global model generated, due to the decentralized and iterative nature of the training process, which often requires manual custom scripting and inefficient use of compute resources.
Innovation Solution
A hierarchical framework is introduced that includes a Visual Data Management System (VDMS) to automate data filtering, node selection, reduce bias, ensure fairness, and efficiently preprocess data, using a query interface to simplify the federated learning process and maintain data privacy by executing subqueries on remote worker nodes without sending sensitive data to a central location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented with decentralized training across multiple worker nodes, then data privacy is improved and training efficiency is enhanced, but the system complexity increases and requires manual custom scripting
Solution Approach 1:
The patent introduces a central coordinator node that acts as an intermediary between worker nodes in the federated learning system. This coordinator manages model aggregation, coordinates training rounds, and handles node selection, thereby reducing the complexity of peer-to-peer interactions while maintaining data privacy. The coordinator receives local model updates from workers and aggregates them into a global model, simplifying the decentralized architecture.
Solution Approach 2:
The patent implements a universal federated learning framework that can handle multiple functions including node selection, data preprocessing, model training, and result aggregation within a single system architecture. This multi-functional approach reduces the need for separate manual scripting for each operation, automating the entire federated learning process while maintaining flexibility across different use cases.
2Adaptability or versatility
If manual custom scripting is used for federated learning processes, then flexibility in handling node selection and data preprocessing is improved, but productivity decreases and resource utilization becomes inefficient
Solution Approach 1:
The patent implements automated self-service mechanisms where the federated learning system automatically performs node selection, data preprocessing, model training, and aggregation without requiring manual intervention. The system adapts to different scenarios through configurable parameters and algorithms, maintaining flexibility while significantly improving productivity by eliminating repetitive manual scripting tasks.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring node selection criteria, data preprocessing pipelines, and training hyperparameters before the federated learning process begins. This preliminary setup allows the system to automatically adapt to different scenarios during training without requiring manual reconfiguration, thereby maintaining flexibility while improving efficiency through automated decision-making.
3Manufacturing precision
If data is preprocessed and filtered before federated learning, then training accuracy is improved and bias is reduced, but the time and computational resources required increase
Solution Approach 1:
The patent applies partial preprocessing by selectively filtering and preprocessing only the most critical data aspects that have the greatest impact on model accuracy and bias reduction. Rather than preprocessing all data exhaustively, the system identifies and processes key features and samples, thereby maintaining high training accuracy while significantly reducing preprocessing time and computational resource requirements.
4Productivity
If federated learning is scaled to multiple worker nodes, then training capacity is improved, but coordination overhead and communication costs increase
Solution Approach 1:
The patent extracts and centralizes the coordination functions (model aggregation, node selection, training orchestration) into a dedicated coordinator node, separating these overhead tasks from the worker nodes. This extraction allows worker nodes to focus solely on local model training, reducing their communication burden and energy consumption while maintaining the benefits of scaled training capacity across multiple distributed nodes.
Data Source
AI summary
Methods, apparatus and articles of manufacture to implement frameworks for training of federated learning models are disclosed. Example apparatus disclosed herein are to cause transmission of a first query to a first worker node of a plurality of worker nodes, the first query based on constraints to train a machine learning model. Disclosed example apparatus are also to cause transmission of a second query to a second worker node of the plurality of worker nodes, the second query based on the constraints. Disclosed example apparatus are further to cause transmission of a third query to the first worker node based on comparison of a first score from the first worker node to a second score from the second worker node, the third query instructing the first worker node to train the machine learning model.