Federated Learning Controller Tree Infrastructure
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Solution Overview
Problem
Current federated learning systems face challenges in scaling and error-prone manual processes for configuring learning tasks and data management, particularly due to data privacy regulations and the need to perform model training on-site without data export.
Innovation Solution
A controller represents computing infrastructure as a tree structure, forming associations between datasets and nodes, allowing for automated provisioning of model training tasks while adhering to data sovereignty and privacy rules, using an infrastructure registry and metadata database to simplify the configuration of machine learning workloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration of learning tasks and data movement is performed, then flexibility in task setup is maintained, but scalability and error rates deteriorate
Solution Approach 1:
The system enables automated self-provisioning of federated learning tasks by having the controller automatically discover available datasets, match them with suitable compute nodes, and configure learning tasks without manual intervention. The controller autonomously manages the entire workflow from task definition to execution, eliminating the need for manual data movement and task configuration while maintaining operational flexibility.
2Productivity
If data is moved to on-site compute nodes for training, then model training can be performed locally, but data privacy compliance becomes more complex
Solution Approach 1:
The controller acts as an intermediary that manages data privacy compliance by orchestrating the federated learning process without requiring direct data movement. It matches datasets with compute nodes based on privacy requirements and data location, configuring tasks so that training occurs locally while the controller maintains oversight of compliance. This intermediary role simplifies privacy management by centralizing the coordination logic.
3Productivity
If automated provisioning is implemented, then scalability improves, but system complexity increases
Solution Approach 1:
The controller is designed as a universal multi-functional system that handles dataset registration, compute node discovery, task matching, and configuration automation through a single integrated architecture. By consolidating these diverse functions into one controller that can perform multiple roles, the system achieves high automation and scalability without proportionally increasing overall system complexity. The controller's ability to handle multiple tasks through a unified interface simplifies the architectural overhead.
Data Source
AI summary
In one embodiment, a controller for a federated learning system represents computing infrastructure for the federated learning system as a tree structure. The controller forms associations between datasets available to the federated learning system and nodes in the tree structure. The controller receives one or more instructions to perform model training in the federated learning system with datasets specified using their associations. The controller configures, in response to the one or more instructions, the federated learning system to perform the model training using the datasets specified by the one or more instructions using the tree structure.