Iterative Learning Interference Mitigation in Federated Networks
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Solution Overview
Problem
Federated learning systems using direct analog modulation for over-the-air communication are susceptible to interference, which affects the efficiency and robustness of the iterative learning process between agent entities and a server entity.
Innovation Solution
A method and system that estimate the level of interference and an acceptable level of interference for the iterative learning process, allowing for the selection and configuration of interference mitigating network operations to enhance communication efficiency and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct analog modulation is used for over-the-air communication in federated learning, then communication efficiency is improved, but susceptibility to interference increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting modulation parameters (such as modulation order, coding rate, and power allocation) based on estimated interference levels. When interference is detected, the system changes these parameters to maintain communication efficiency while adapting to the degraded channel conditions, thus resolving the contradiction between efficiency and reliability.
Solution Approach 2:
The system implements dynamics by making the communication parameters adaptive rather than static. The modulation and coding schemes are dynamically selected based on real-time interference estimation, allowing the system to optimize performance under varying interference conditions while maintaining robustness against unpredictable channel variations.
2Reliability
If interference mitigating network operations are configured, then robustness against interference is improved, but device complexity increases
Solution Approach 1:
The system applies self-service by implementing autonomous interference estimation and automatic selection of mitigation operations. The server entity independently estimates interference levels and selects appropriate mitigation strategies without requiring manual configuration or complex coordination between network elements, thereby improving robustness while minimizing the increase in device complexity.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors communication quality, estimates interference levels, and adjusts mitigation operations accordingly. This closed-loop approach allows the system to automatically adapt to changing interference conditions, maintaining robustness through simple, rule-based feedback rather than complex centralized control.
3Productivity
If iterative learning process continues in high interference scenarios, then learning convergence is improved, but communication resource consumption increases
Solution Approach 1:
The system applies periodic action by implementing scheduled interruptions of the iterative learning process during periods of high interference. Instead of continuously transmitting, the system periodically pauses communication when interference exceeds acceptable thresholds, allowing the learning algorithm to continue processing locally while conserving radio resources. This resolves the contradiction by maintaining convergence progress through periodic updates rather than continuous resource consumption.
Data Source
AI summary
A method for iterative learning is performed by a server entity communicating with the agent entities over a radio propagation channel. An estimate of a level of interference of the radio propagation channel is obtained. An estimate of an acceptable level of interference for performing at least one iteration of the iterative learning process with the agent entities is obtained. An interference mitigating network operation is selected from a set of available interference mitigating network operations. The interference mitigating network operation is selected as a function of the estimate of the acceptable level of interference and the estimate of the level of interference. In accordance with the interference mitigating network operation, at least one of the server entity, the agent entities, a network node causing the level of interference level, is configured. At least one iteration of the iterative learning process is performed with the agent entities.