Federated Learning Agent Reporting Schedule

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Solution Overview

Problem

Federated learning experiences inefficiency due to latency in the iterative process, where agents must wait for the next model parameter vector to be broadcasted before performing SGD steps, leading to delayed communication and reduced learning efficiency.

Innovation Solution

Configuring agent entities with a reporting schedule that allows them to base their computations on results from other agents, enabling them to report computational results in an ordered sequence, thereby reducing latency and improving communication efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If agents wait for the next model parameter vector to be broadcasted before performing SGD steps, then the iterative learning process maintains synchronization and model consistency, but reporting latency increases and learning efficiency decreases

Engineering Contradiction:

Improvemodel consistencyVSAvoidreporting latency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

Agents perform SGD steps using the current model parameter vector before the next vector is broadcasted. This preliminary computation allows agents to work ahead, reducing waiting time while maintaining model consistency through proper aggregation of results.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transitions from static waiting (agents idle until next broadcast) to dynamic computation (agents continuously performing SGD steps). Agents dynamically utilize their local data and compute model updates whenever new parameter vectors are received, eliminating idle time.

Inventive Principle:

Principle #15Dynamics

2Ease of operation

If agents perform computations independently without utilizing other agents' results, then computational simplicity is maintained, but communication efficiency decreases and more iterations are required

Engineering Contradiction:

Improvecomputation simplicityVSAvoidlearning efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

Agents merge their independently computed model updates with updates from other agents through the server's aggregation process. This combining of parallel computations accelerates convergence while maintaining the simplicity of individual agent operations.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system implements feedback loops where agents receive aggregated model updates from the server and use them to refine their local computations. This feedback mechanism enables agents to leverage collective learning progress while maintaining computational independence.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240303500A1Server and agent for reporting of computational results during an iterative learning process

Publication Date: 2024.09.12 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

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 an order according to which the agent entities are to report computational results of the computational task. The agent entities are configured to, per each iteration of the learning process, base their computation of the computational task on any computational result of the computational task received from any other of the agent entities prior to when the agent entities themselves are scheduled to report their own computational results for that iteration. The method comprises performing the iterative learning process with the agent entities according to the reporting schedule and until a termination criterion is met.