Dynamic Update Resolution Signaling to Reduce Federated Learning Overhead

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Solution Overview

Problem

Existing wireless communication systems face challenges in managing update resolution during federated learning procedures, leading to unnecessary communication overhead and potential network performance impacts due to fixed update resolutions.

Innovation Solution

Implementing update resolution signaling mechanisms that allow UEs and base stations to dynamically adjust the accuracy of machine learning component updates based on factors such as UE capabilities and message payload size, reducing communication overhead and improving network performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If fixed update resolution is used in federated learning, then implementation simplicity is maintained, but communication overhead increases and network performance deteriorates

Engineering Contradiction:

Improveimplementation simplicityVSAvoidcommunication overhead

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The patent implements dynamic update resolution adjustment where the resolution parameter is adapted based on UE capabilities, channel conditions, and federated learning round progress. The base station receives capability information from UEs and configures appropriate update resolutions dynamically, allowing the system to transition from static to adaptive operation, thereby reducing communication overhead while maintaining implementation feasibility through standardized signaling procedures.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If high update resolution is used, then machine learning update accuracy is improved, but communication overhead increases

Engineering Contradiction:

Improvemachine learning update accuracyVSAvoidcommunication overhead

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent changes the resolution parameter of update signals dynamically based on operational conditions. Different precision levels (e.g., 8-bit, 4-bit, 2-bit quantization) are selected according to UE capabilities, channel quality, and training progress. This parameter adaptation allows the system to achieve high accuracy when conditions permit while reducing communication overhead when resources are constrained, directly addressing the trade-off between precision and quantity.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies different update resolutions to different UEs or different federated learning rounds based on local conditions. Each UE can be configured with an update resolution appropriate to its capabilities and channel conditions, rather than using a uniform resolution for all devices. This localized optimization ensures that each participant contributes with appropriate precision without unnecessarily increasing overall communication overhead.

Inventive Principle:

Principle #3Local quality

3Loss of energy

If dynamic update resolution adjustment is implemented, then communication overhead is reduced, but system complexity increases

Engineering Contradiction:

Improvecommunication overheadVSAvoidsystem complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The patent implements feedback mechanisms where UEs report their capabilities and channel conditions to the base station, which then configures appropriate update resolutions. The base station monitors performance and can adjust resolution parameters dynamically based on observed conditions. This feedback-driven approach automates the complexity management, allowing dynamic resolution adjustment without requiring complex manual configuration or coordination overhead.

Inventive Principle:

Principle #23Feedback

4Productivity

If update resolution is optimized for each UE, then network performance is improved, but signaling overhead increases

Engineering Contradiction:

Improvenetwork performanceVSAvoidsignaling overhead

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The patent performs preliminary capability assessment and resolution configuration before actual federated learning updates begin. UEs report their capabilities in advance, and the base station pre-configures appropriate update resolutions based on this information and expected channel conditions. This preliminary setup reduces the need for continuous signaling during the learning process, as the resolution parameters are established beforehand and can be adjusted only when conditions significantly change.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12356219B2Update resolution signaling in federated learning

Publication Date: 2025.07.08 QUALCOMM INC

AI summary

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may determine an update resolution associated with providing, to a base station during a federated learning procedure, an update associated with a machine learning component. The UE may transmit the update to the base station based at least in part on the update resolution. Numerous other aspects are provided.