Observed Environmental Vector Feedback for Client-Specific Wireless ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently utilizing machine learning components due to varying environmental conditions and resource consumption in reporting observed environmental vectors, particularly in federated learning scenarios where clients operate in diverse environments.
Innovation Solution
Implementing conditioning networks and autoencoders with observed environmental vector feedback mechanisms to adapt machine learning components for individual clients, reducing resource consumption by compressing and reconstructing data using client-specific parameters determined by conditioning vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clients report observed environmental vectors in federated learning scenarios, then machine learning components can be adapted to individual client environments, but resource consumption increases due to varying environmental conditions and diverse client operations
Solution Approach 1:
The patent applies local quality by determining client-specific parameters through conditioning vectors that are tailored to each client's local environment. The conditioning network processes observed environmental vectors locally at each client device, creating customized conditioning vectors that reflect local conditions rather than using a universal approach. This enables the machine learning components to adapt to individual client environments while avoiding the resource overhead of centralized processing for all clients.
Solution Approach 2:
The patent implements preliminary action by pre-processing observed environmental vectors through conditioning networks to generate conditioning vectors before the main machine learning inference. The conditioning vectors are determined in advance based on environmental observations, and these pre-computed vectors are then used to condition the machine learning components. This preliminary processing step enables efficient adaptation without requiring resource-intensive real-time adjustments during inference.
2Use of energy by moving object
If conditioning networks and autoencoders are implemented to compress and reconstruct data, then resource consumption is reduced, but system complexity increases due to additional machine learning components
Solution Approach 1:
The patent introduces intermediary components (conditioning networks and autoencoders) that act as mediators between the observed environmental vectors and the main machine learning components. The conditioning network processes environmental observations and generates conditioning vectors that mediate the interaction between environment and model. The autoencoder mediates the compression and reconstruction of these vectors, transforming high-dimensional environmental data into compact representations. These intermediaries reduce the computational burden on the main machine learning components while managing the complexity through modular, specialized sub-networks.
Solution Approach 2:
The patent applies segmentation by dividing the machine learning system into distinct functional modules: the conditioning network that processes environmental vectors, the autoencoder that compresses and reconstructs data, and the main machine learning components that perform inference. This segmentation allows each module to be optimized independently for its specific function, reducing overall resource consumption while managing complexity through clear separation of concerns. The conditioning vector serves as a segmented representation that captures essential environmental information without requiring the full complexity of raw environmental data.
Data Source
AI summary
Various aspects of the present disclosure relate to wireless communication. In some aspects, a client may receive an observed environmental vector feedback configuration associated with a reporting procedure for reporting updates corresponding to at least one observed environmental vector that is based at least in part on one or more features associated with an environment of the client, wherein the observed environmental vector comprises input for a first machine learning component that determines client specific parameters for use by a second machine learning component. The client may transmit an update corresponding to the at least one observed environmental vector based at least in part on the observed environmental vector feedback configuration. Numerous other aspects are provided.