Federated Learning Agent Pairing for Resilient Reporting
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Solution Overview
Problem
Federated learning systems face challenges in obtaining updates from all agents due to broken communication links between agents and the parameter server, leading to inefficiencies and reduced resilience in iterative learning processes.
Innovation Solution
A method where the server entity configures agent entities with a reporting schedule that pairs them to superimpose computational results, allowing each agent to report to both the server and its pair, ensuring that the server receives updates even in situations of broken links, thereby enhancing communication resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agents report computational results directly to the server using traditional federated learning, then the communication process is simple, but the server cannot obtain updates from agents with broken communication links
Solution Approach 1:
Relay agents are introduced as intermediaries between non-relay agents and the server. Non-relay agents transmit their computational results to relay agents, which then forward these results to the server. This intermediary mechanism ensures that even when direct communication links between certain agents and the server are broken, the computational results can still be delivered through alternative paths via relay agents, thereby improving the reliability of update obtainment.
2Productivity
If agents transmit computational results individually to the server, then the communication protocol is simple, but the communication efficiency is reduced due to broken links
Solution Approach 1:
Multiple computational results from different agents are merged and transmitted together through relay agents. Instead of each agent transmitting its results individually to the server (which would be inefficient and vulnerable to link failures), the relay agents aggregate and forward multiple results in combined transmissions. This merging approach improves communication efficiency by reducing the total number of transmissions required and enhances reliability by providing redundant transmission paths.
3Measurement precision
If the server requests updates from all agents in each iteration, then the learning accuracy is maintained, but the signaling overhead increases
Solution Approach 1:
Relay agents pre-process and prepare computational results for forwarding before the server requests them. During each iteration, relay agents collect and organize computational results from non-relay agents in advance, so that when the server needs updates, the information is already prepared and available for efficient transmission. This preliminary action reduces the signaling overhead by minimizing the back-and-forth communication required between the server and agents while ensuring that accurate learning updates are still obtained.
Data Source
AI summary
There is provided mechanisms for configuring agent entities with a reporting schedule 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 schedule. The reporting schedule defines pairs of the agent entities. According to the reporting schedule and per each iteration of the learning process, each of the agent entities in each pair is to report its own computational result of the computational task to both the server entity and the other of the agent entities in the same pair. When reporting its own computational result to the server entity, each of the agent entities is to superimpose the computational result of the other of the agent entities in the same pair. The method comprises performing the iterative learning process with the agent entities until a termination criterion is met.