Federated Learning Topology Reconfiguration via Workload Abstraction
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Solution Overview
Problem
Machine learning workloads in federated learning systems are complex and cumbersome due to tight coupling with infrastructure resource management and machine learning libraries, requiring extensive and error-prone code updates for topology and algorithm changes.
Innovation Solution
A system that represents machine learning workloads through a layer of abstraction, allowing for dynamic topology reconfiguration by decomposing workloads into primitives and decoupling AI/ML algorithms from infrastructure, enabling simplified and declarative workload composition and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine learning workloads are tightly coupled with infrastructure resource management and machine learning libraries, then the system can maintain straightforward implementation, but the system becomes complex and cumbersome when topology or algorithm changes are needed
Solution Approach 1:
The patent segments the machine learning workload into distinct components: infrastructure resource management, machine learning library operations, and topology/configuration parameters. This segmentation allows each component to be independently managed and modified without affecting the others, resolving the contradiction by enabling easy implementation through modular architecture while reducing system complexity when changes are needed.
Solution Approach 2:
The patent extracts topology and configuration parameters from the tightly coupled infrastructure and library code, placing them in separate configurable layers. This extraction allows topology changes and algorithm modifications without requiring source code updates, thereby maintaining ease of implementation while significantly reducing system complexity for reconfiguration.
2Adaptability or versatility
If extensive code updates are made to change topology or algorithms in the learning system, then the system can adapt to new requirements, but the process becomes error-prone and cumbersome
Solution Approach 1:
The patent implements dynamic configuration parameters that allow topology and algorithm changes without code modification. The system transitions from static, code-embedded configurations to dynamic, externally-configurable parameters, enabling adaptability to new requirements while maintaining reliability by eliminating error-prone manual code updates.
Solution Approach 2:
The patent changes the system from code-based configuration to parameter-based configuration. Topology and algorithm changes are achieved by modifying configuration parameters rather than source code, which enhances adaptability while ensuring reliability through validated parameter interfaces and automated configuration management.
3Adaptability or versatility
If source code updates are performed for different machine learning workload types, then the system can support diverse workloads, but the process becomes time-consuming
Solution Approach 1:
The patent creates a universal configuration framework that handles multiple machine learning workload types through a single set of configurable parameters. This universal approach eliminates the need for separate source code updates for different workload types, maintaining versatility while dramatically reducing configuration time through parameter-based management.
4Adaptability or versatility
If configuration updates are made in an ad-hoc fashion for different machine learning workloads, then the system can accommodate varying needs, but the process becomes error-prone
Solution Approach 1:
The patent introduces an intermediary configuration layer between the machine learning workloads and the underlying infrastructure. This intermediary layer provides standardized parameter interfaces that mediate between diverse workload requirements and system implementation, enabling workload-specific customization while improving configuration accuracy through validated parameter management.
Data Source
AI summary
In one embodiment, a device provides, to a user interface, data representing a topology of a federated learning system configured across nodes in a computer network. Each node in the topology has an assigned role and is connected to at least one other node via a connector that is dependent on its assigned role. The device receives, via the user interface, a requested change to the topology of the federated learning system. The device selects, based on assigned roles of those nodes affected by the requested change to the topology of the federated learning system, code for execution by those nodes. The device implements the requested change to the topology of the federated learning system in part by sending the code selected by the device to those nodes affected by the requested change.