Federated Reinforcement Learning Agents for Dynamic Network KPIs
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Solution Overview
Problem
Conventional federated learning methods introduce bias through simple averaging of node models and lack timely and accurate results in dynamic environments, while reinforcement learning systems fail to retain data locally and interact effectively with environments.
Innovation Solution
A Federated Reinforcement Learning (FRL) system that groups observations into similar sets to train specialized machine learning agents, allowing a central node to combine models for improved accuracy and responsiveness in dynamic situations like communication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional federated learning uses simple averaging of node models, then the system is easy to implement, but the model accuracy deteriorates due to introduced bias from unbalanced local datasets
Solution Approach 1:
The patent changes the aggregation parameter from simple averaging to a weighted aggregation that accounts for data distribution characteristics. By introducing data quality weights and distribution similarity metrics, the system adjusts the aggregation parameters to resolve the bias introduced by unbalanced local datasets, thereby improving model accuracy while maintaining implementation feasibility.
Solution Approach 2:
The patent replaces the mechanical simple averaging mechanism with a more sophisticated weighted aggregation mechanism that incorporates data quality assessment and distribution similarity calculations. This substitution eliminates the bias inherent in uniform averaging while maintaining computational feasibility through structured algorithms.
2Reliability
If conventional federated learning trains models locally at worker nodes, then data privacy is protected, but the system cannot provide timely and accurate results in dynamic environments
Solution Approach 1:
The patent introduces feedback mechanisms where worker nodes continuously monitor local environment changes and communicate relevant information to the central node. This feedback loop enables the system to adapt to dynamic environments while maintaining data privacy, as only processed insights rather than raw data are shared, allowing timely responses to changing conditions.
Solution Approach 2:
The patent makes the federated learning system dynamic by enabling continuous adaptation to changing environmental conditions. The system dynamically adjusts model training based on real-time feedback from worker nodes about environment changes, allowing it to provide timely and accurate results while maintaining the privacy benefits of local data processing.
3Productivity
If reinforcement learning systems interact directly with environments, then the system achieves high responsiveness, but it fails to retain data locally and effectively
Solution Approach 1:
The patent segments the system into centralized and distributed components, where the central node handles environment interaction and data aggregation, while worker nodes perform local processing and model training. This segmentation allows the system to maintain data locally at worker nodes while achieving high responsiveness through coordinated interaction with the central node, resolving the contradiction between data retention and environmental interaction capability.
Data Source
AI summary
Methods and central nodes for developing machine-learning models, the method including receiving, at a central node, at least one episode including a plurality of changes of an environment. The method further includes analysing the episode to extract observations and grouping the observations from among the plurality of observations into a plurality of groups of similar observations. A first machine learning agent is then trained using a first group of similar observations from among the plurality of groups of similar observations, and a second machine learning agent is trained using a second group of similar observations from among the plurality of groups of similar observations, wherein the second group of similar observations is different to the first group of similar observations. The central node obtains a central machine-learning model based on an output from at least one of the trained first machine learning agent and the trained second machine learning agent.