Federated Learning Topology Adaptation via State Monitoring
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Solution Overview
Problem
Federated learning systems face inefficiencies due to heterogeneity among participating clients and jobs, leading to slower training convergence and suboptimal deployment configurations, which are cumbersome to manage and often result in deployment failures.
Innovation Solution
A controller in the federated learning system collects state information from nodes, determines topology adjustments, selects affected nodes, and sends instructions to implement these adjustments, allowing for adaptive reconfiguration of resources and simplified workload management using a role abstraction model that decouples AI/ML algorithms from infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration and reconfiguration decisions are made by the administrator, then deployment control is maintained, but the process becomes cumbersome and error-prone
Solution Approach 1:
The system performs self-configuration and self-optimization by automatically analyzing client heterogeneity and adjusting deployment parameters without requiring manual administrator intervention. The controller autonomously makes reconfiguration decisions based on system state, eliminating the cumbersome manual process while maintaining deployment control.
Solution Approach 2:
The system continuously monitors system state information from clients and uses this feedback to dynamically adjust deployment configurations. This closed-loop approach enables automatic optimization of resource allocation and topology based on real-time conditions, replacing manual reconfiguration decisions with automated feedback-driven adjustments.
2Productivity
If the system topology is manually reconfigured to optimize training efficiency, then training performance improves, but deployment failures and suboptimal choices increase
Solution Approach 1:
The controller autonomously analyzes system state and performs self-optimization of topology and resource allocation without manual intervention. This automated self-service approach eliminates human error and ensures reliable deployments while maintaining optimal training efficiency through continuous adaptation to client heterogeneity.
Solution Approach 2:
The system dynamically adjusts deployment parameters such as resource allocation, topology configuration, and client grouping based on analyzed system state. These automated parameter changes optimize training efficiency while maintaining deployment reliability through systematic, data-driven adjustments rather than manual reconfiguration.
3Adaptability or versatility
If the system accommodates heterogeneity among clients and jobs, then real-world applicability improves, but training convergence slows down
Solution Approach 1:
The system applies different deployment strategies and resource allocations tailored to local client characteristics and job requirements. By customizing configurations for each client based on its specific heterogeneity attributes, the system maintains real-world applicability while optimizing training convergence for each participant's unique conditions.
Solution Approach 2:
The system dynamically adjusts topology and resource configuration in response to changing system state and client conditions. This dynamic adaptation enables the system to accommodate client heterogeneity while maintaining optimal training convergence speed through continuous reconfiguration based on real-time system state analysis.
Data Source
AI summary
In one embodiment, a controller obtains state information from a plurality of nodes in a federated learning system. The controller determines, based on the state information, an adjustment to a topology of the federated learning system. The controller selects one or more nodes from among the plurality of nodes affected by the adjustment. The controller sends instructions to the one or more nodes, to implement the adjustment to the topology of the federated learning system.