Gradient Accumulation for Federated Learning OTA Aggregation
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G NR, face challenges in efficiently aggregating local model updates in federated learning due to high communication overhead and information loss from digital transmission, especially in scenarios where channel inversion is inefficient or not desirable.
Innovation Solution
Implementing over-the-air (OTA) aggregation with truncated channel inversion and local model update accumulation, where edge devices transmit a sum of local model updates and errors over multiple access channels, reducing communication overhead and mitigating information loss through analog signaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital transmission is used for federated learning model updates, then communication reliability is improved, but communication overhead increases and information loss occurs
Solution Approach 1:
The patent replaces digital transmission mechanisms with analog over-the-air aggregation. Instead of converting model updates to digital bits for transmission, the system uses analog signaling where multiple device updates are superimposed in the wireless channel, directly aggregating gradients without digital encoding/decoding operations that cause information loss
Solution Approach 2:
The patent introduces a base station as an intermediary that performs centralized coordination including gradient aggregation, model updating, and error compensation management. The base station receives analog signals from multiple devices, aggregates gradients computationally, and manages the accumulation of truncation errors across training rounds
2Productivity
If channel inversion is applied for OTA aggregation, then transmission efficiency is improved, but system complexity increases and it may not be desirable in all scenarios
Solution Approach 1:
The patent applies truncated channel inversion that selectively inverts channel coefficients above a threshold while leaving others unchanged. This partial application reduces computational complexity and avoids the full channel inversion operation, making the system more practical while still providing benefits in favorable channel conditions
Solution Approach 2:
The patent implements dynamic adjustment of channel inversion parameters including threshold selection and inversion factors. The system can adaptively adjust these parameters based on channel conditions, device capabilities, and training requirements, allowing flexibility to reduce complexity when full inversion is not necessary
3Loss of information
If local model update errors are truncated to reduce transmission data, then communication overhead is reduced, but model accuracy deteriorates due to information loss
Solution Approach 1:
The patent performs preliminary computation of local model updates at each device, then truncates less significant error components before transmission. By pre-identifying and retaining only the most important gradient information, the system reduces transmission overhead while preserving essential model learning signals
Solution Approach 2:
The patent implements feedback mechanisms where the base station monitors model convergence and transmission quality, then adjusts truncation thresholds and aggregation parameters accordingly. This feedback loop allows the system to maintain model accuracy by adapting the level of truncation based on actual training performance and error patterns
4Loss of time
If multiple devices transmit simultaneously over shared resources, then communication time is reduced, but interference and signal quality deteriorate
Solution Approach 1:
The patent merges multiple device transmissions into a single analog superposition signal that is received and processed collectively by the base station. Instead of scheduling separate time slots for each device, the system combines all device updates in the wireless medium, achieving time efficiency while using computational aggregation to separate and process individual contributions
Data Source
AI summary
A UE may identify, in each round other than an initial round, a first plurality of local model update elements of a present round. The first plurality of local model update elements of the present round may be associated with an updated local machine learning model. The UE may transmit to a base station, in each round other than the initial round, over a multiple access channel via analog signaling, a second plurality of local model update elements of the present round based on a third plurality of local model update elements of the present round. The third plurality of local model update elements of the present round may correspond to a sum of the first plurality of local model update elements of the present round and a local model update error of a previous round immediately before the present round. The analog signaling may be associated with OTA aggregation.