Received-Power Grouping for OTA Federated Aggregation
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Solution Overview
Problem
Conventional over-the-air aggregation in federated learning faces challenges in achieving tight power control among user equipment due to varying distances and channel conditions, leading to unequal received signal magnitudes and inefficient communication costs.
Innovation Solution
A parameter server selects groups of user equipment based on common received power properties, such as pathloss or power budget, to perform over-the-air aggregation, and user equipment performs truncated channel inversion based on power headroom and thresholds for power control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional over-the-air aggregation is used without power control, then device complexity is reduced, but signal magnitude consistency deteriorates due to varying distances and channel conditions
Solution Approach 1:
The system dynamically adjusts transmission power parameters based on channel conditions and received power properties. The parameter server modifies power control parameters for each UE based on its received power characteristics, enabling consistent signal magnitudes at the parameter server while managing device complexity through coordinated control.
Solution Approach 2:
The system implements feedback mechanisms where UEs report power headroom and received power properties to the parameter server. Based on this feedback, the parameter server adjusts power control parameters and selects appropriate UEs for aggregation sessions, ensuring signal consistency without requiring complex local power control at each device.
2Productivity
If power control is implemented to ensure consistent signal magnitudes, then communication efficiency is improved, but device complexity increases due to power control mechanisms
Solution Approach 1:
The system segments UEs into groups based on their received power properties and divides the power control function between the parameter server and individual UEs. The parameter server handles high-level power control decisions while UEs perform localized power adjustments, improving communication efficiency without concentrating all complexity at a single device.
Solution Approach 2:
The parameter server acts as an intermediary that coordinates power control across multiple UEs. It receives power headroom reports, determines appropriate power levels, and transmits power control parameters to UEs, thereby managing the complexity centrally while maintaining efficient communication.
3Loss of energy
If all UEs participate in over-the-air aggregation, then communication cost is reduced, but power control difficulty increases due to varying channel conditions
Solution Approach 1:
The system assigns different roles and power control parameters to different UEs based on their individual channel conditions and received power properties. UEs with better channel conditions may be selected for aggregation while others provide feedback or assist in power control, enabling inclusive participation without uniformly increasing power control difficulty.
Solution Approach 2:
The system selectively includes UEs in over-the-air aggregation sessions based on their power properties and channel conditions. Not all UEs must participate in every session, allowing the system to achieve sufficient communication efficiency while avoiding the excessive complexity of coordinating all devices simultaneously.
Data Source
AI summary
A parameter server located at a base station may coordinate federated learning among multiple user equipment (UEs) using over-the-air (OTA) aggregation with power control to mitigate aggregation distortion due to amplitude misalignment. The parameter server may select a first group of UEs for a first OTA aggregation session of a federated learning round based on a common received power property of each UE in the first group of UEs. The parameter server may transmit a global model to the first group of UEs. Each UE in the first group may train the global model based on a local dataset and transmit values associated with the trained local model. The parameter server may receive, on resource elements for the first group of UEs, a first aggregate amplitude modulated analog signal representing a combined response from the first group of UEs.