Federated Learning Signaling in Wireless Networks
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Solution Overview
Problem
Current wireless communication technologies, such as LTE and NR, face challenges in efficiently supporting the increasing demand for mobile broadband access, particularly in integrating machine learning components and facilitating federated learning in cellular networks, which affects network performance and user experience.
Innovation Solution
Implementing a federated learning configuration in wireless communication systems, where base stations transmit parameters to user equipment (UEs) for local training, and UEs transmit local updates back to the base stations, enabling predictable scheduling and improved network performance through machine learning component updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional wireless communication protocols (LTE/NR) are used for machine learning updates, then network coverage and basic connectivity are maintained, but network performance deteriorates due to inefficient signaling and resource usage
Solution Approach 1:
The patent introduces new wireless signaling parameters and message structures specifically designed for federated learning communications. The base station transmits configuration messages containing federated learning parameters (such as update intervals, resource allocations, and aggregation timing) that optimize the machine learning update process while reducing unnecessary signaling overhead compared to traditional protocols
Solution Approach 2:
The patent segments the federated learning communication process into distinct phases with dedicated signaling messages: initial configuration phase, update transmission phase, and aggregation coordination phase. This segmentation allows each phase to use optimized signaling formats, improving overall efficiency while reducing the complexity of handling all operations with a single protocol structure
2Reliability
If machine learning components are updated frequently across UEs, then model accuracy and user experience improve, but wireless resource consumption increases
Solution Approach 1:
The patent implements periodic update mechanisms where UEs transmit local model updates at configured intervals rather than continuously. The base station sends configuration messages specifying update timing and triggers, allowing the system to balance model accuracy requirements with wireless resource conservation by updating only when necessary or at optimal periods
Solution Approach 2:
The patent establishes feedback loops where the base station monitors update quality and network conditions, then adjusts configuration parameters for subsequent update cycles. This feedback mechanism ensures model accuracy is maintained while optimizing resource usage by adapting update frequency and timing based on actual performance metrics and network state
3Loss of information
If federated learning procedures are implemented in cellular networks, then data privacy is maintained through local training, but network integration complexity increases
Solution Approach 1:
The patent positions the base station as an intermediary that manages the federated learning process between the network core and UEs. The base station handles configuration distribution, update collection, and coordination without requiring direct complex interactions between UEs and the core network, thereby simplifying network integration while maintaining the privacy-preserving local training architecture
Solution Approach 2:
The patent designs the base station to perform multiple functions: traditional wireless communication management plus federated learning coordination. By making the base station a multi-functional entity that can handle both conventional traffic and machine learning updates through unified signaling mechanisms, the patent reduces overall network integration complexity compared to adding separate dedicated infrastructure
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a base station may transmit, to a user equipment (UE), a federated learning configuration that indicates one or more parameters of a federated learning procedure associated with a machine learning component. The base station may receive a local update associated with the machine learning component from the UE based at least in part on the federated learning configuration. Numerous other aspects are provided.