Federated Learning Agent Reporting via Importance Thresholds
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Solution Overview
Problem
Centralized scheduling in federated learning systems incurs significant signaling overhead and communication costs due to the need for the parameter server to collect L2-norms and channel estimates from all agents, especially as the number of participating agents grows.
Innovation Solution
Configuring agent entities to contend for channel access to report computational results only when an importance metric satisfies a reporting condition, allowing them to decide based on their own quality metrics, thereby reducing the need for excessive information exchange and signaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized scheduling is used where the parameter server collects L2-norms and channel estimates from all agents, then scheduling decisions can be made based on comprehensive information, but signaling overhead and communication costs increase significantly
Solution Approach 1:
Each agent autonomously determines whether to report its computational results by comparing its importance metric against a threshold, eliminating the need for the parameter server to collect and process L2-norms and channel estimates from all agents. This self-service mechanism reduces signaling overhead while maintaining scheduling effectiveness.
Solution Approach 2:
The patent extracts only the essential information (computational results with importance metrics above threshold) that needs to be transmitted to the parameter server, rather than transmitting all agent states including L2-norms and channel estimates. This selective extraction reduces communication volume while preserving scheduling decision quality.
2Loss of information
If all agents report computational results in every iteration, then the parameter server has complete information for aggregation, but communication costs and signaling overhead increase
Solution Approach 1:
Instead of requiring all agents to report in every iteration (excessive action), the patent implements a threshold-based mechanism where only agents with computational results meeting the importance criterion report. This partial action approach maintains sufficient information for model convergence while significantly reducing communication overhead.
Solution Approach 2:
The patent changes the reporting parameter from a binary all-or-nothing approach to a continuous threshold-based approach. Agents compare their importance metrics against a threshold parameter, and only those exceeding the threshold report. This parameter transformation enables selective reporting that balances information completeness with communication efficiency.
3Productivity
If the parameter server collects information from all agents for centralized scheduling, then optimal scheduling decisions can be made, but the system complexity and processing requirements at the server increase
Solution Approach 1:
Agents perform the scheduling decision-making function locally by autonomously determining whether their results meet the reporting threshold, transferring the computational burden from the centralized server to distributed agents. This reduces server processing complexity while maintaining scheduling efficiency.
Solution Approach 2:
The centralized scheduling function is segmented and distributed to individual agents. Each agent independently evaluates its own results against the threshold criterion, dividing the complex centralized decision-making process into simpler distributed decisions. This segmentation reduces server complexity while preserving overall scheduling effectiveness.
Data Source
AI summary
There is provided mechanisms for configuring agent entities with a reporting condition for reporting computational results during an iterative learning process. A method is performed by a server entity. The method comprises configuring the agent entities with a computational task and a reporting condition. The agent entities are to contend for channel access to report computational results of the computational task to the server entity only when an importance metric satisfies the reporting condition. The method comprises performing the iterative learning process with the agent entities until a termination criterion is met.