Central Node Aggregation Using Cross-Discrimination in Federated Learning
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Solution Overview
Problem
Conventional federated learning techniques fail to accurately aggregate machine learning models from decentralized data with varying training data distributions, leading to undesired accuracy degradation, especially when local models are heterogeneous or require capacity growth.
Innovation Solution
Employ cross-discrimination values to determine data distribution correspondence between local models, using generative and discriminative functions to generate synthetic data for model composition, allowing aggregation of heterogeneous models and capacity growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained at each network node locally, then model accuracy and adaptability to local conditions improve, but computational resources and energy consumption at each node increase
Solution Approach 1:
The system segments the machine learning computation into two parts: local data processing at network nodes and centralized model aggregation at the cloud server. Each node performs lightweight local training and only transmits necessary model updates, reducing individual node energy consumption while maintaining overall model accuracy through distributed computation.
Solution Approach 2:
The cloud server acts as an intermediary that receives model parameters from multiple network nodes, aggregates them into a unified global model, and distributes the updated model back to nodes. This intermediary approach allows local nodes to benefit from collective intelligence without each node bearing the full computational burden alone.
2Adaptability or versatility
If machine learning models are trained at each network node locally, then adaptability to local traffic patterns improves, but network bandwidth consumption increases due to model parameter transmission
Solution Approach 1:
The system extracts only the essential model parameters and updates from each node's local machine learning model for transmission to the cloud server. By taking out only the necessary information rather than transmitting complete models or all raw data, the system maintains adaptability to local conditions while minimizing network bandwidth consumption.
Solution Approach 2:
Instead of transmitting complete model datasets or all node information to the cloud, the system performs partial action by sending only the computed model parameters and updates. This partial transmission approach suffices for the cloud server to aggregate and refine the global model without exhausting network bandwidth.
3Device complexity
If a centralized machine learning model is used across the network, then model management and updates are simplified, but the model cannot adequately adapt to diverse local network conditions
Solution Approach 1:
The system implements dynamic model management where the centralized cloud server continuously receives updates from multiple nodes, aggregates their local adaptations, and pushes refined models back. This dynamic process allows the model to evolve and adapt to diverse local conditions over time while maintaining centralized management control.
Solution Approach 2:
The centralized cloud server performs multiple functions: it manages the global model, aggregates parameters from diverse nodes, refines the model through processing, and distributes updated models to all nodes. This multi-functional approach simplifies management while enabling the model to adapt to various local network conditions through collective node input.
Data Source
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AI summary
A method for enabling a machine learning model to be aggregated from local machine learning models comprised in at least two local nodes in a wireless communications network is provided. The method comprises receiving, from each of the at least two local nodes, a parametrized function of a local machine learning model, a generator function of a local generative model, and a discriminator function of a local discriminative model, wherein the generator function and the discriminator function are trained on the same data as the parametrized function. The method also comprises determining, for each pair of the at least two local nodes, a first cross-discrimination values by applying the received discriminator function from a first local node of the pair on samples generated using the received generator function from the second local node of the pair, and a second cross-discrimination value by applying the received discriminator function from the second local node of the pair on samples generated using the received generator function from the first local node of the pair. The method further comprises obtaining an aggregated machine learning model based on the determined first and second cross-discrimination values. Furthermore, the method comprises transmitting information indicating the obtained aggregated machine learning model to one or more of the at least two local nodes in the wireless communications network.