Gradient-Aware Channel Inversion for OTA Federated Learning
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Solution Overview
Problem
Existing wireless communications systems face inefficiencies in over-the-air (OTA) model aggregation for federated learning due to the lack of accurate coordination and configuration of channel inversion coefficients, particularly when gradients from non-stationary user equipment (UEs) are involved, leading to degraded system performance.
Innovation Solution
A method and apparatus for user equipment (UE) to generate local gradients, calculate gradient Sum-Power levels, determine channel inversion coefficients based on scaling factors, and apply analog modulation to form unencoded uplink signals for over-the-air computation of global gradients in federated learning tasks, using network-configured mappings and scaling factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OTA aggregation methods are used without gradient-aware configuration, then the system can perform basic model aggregation, but the accuracy of channel inversion coefficients deteriorates leading to reduced aggregation efficiency
Solution Approach 1:
The patent changes the parameter used for channel inversion coefficient determination from traditional CSI-based approaches to gradient Sum-Power levels. By calculating the sum of squared gradient magnitudes and using this to determine scaling factors and channel inversion coefficients, the system achieves both accurate coefficient determination and efficient aggregation, resolving the contradiction between precision and productivity.
2Productivity
If gradient Sum-Power levels are used to determine channel inversion coefficients, then OTA aggregation efficiency improves, but the complexity of coordinate and configure channel inversion coefficients increases
Solution Approach 1:
The patent implements self-service by having each UE autonomously calculate its own gradient Sum-Power level and determine its own channel inversion coefficient based on the received mapping. This eliminates the need for complex centralized coordination while maintaining high aggregation efficiency, as each device independently performs the necessary computations using its local gradient information.
Solution Approach 2:
The patent replaces complex mechanical coordination mechanisms with a simpler mathematical mapping approach. Instead of requiring complex interaction and coordination between UEs and the network for channel inversion coefficient determination, the system uses a pre-configured mapping between gradient Sum-Power levels and scaling factors, substituting mechanical coordination with a straightforward computational relationship.
3Reliability
If analog modulation with channel inversion coefficients is applied, then gradient transmission effectiveness improves, but the difficulty of detecting and measuring gradient Sum-Power levels increases
Solution Approach 1:
The patent applies preliminary action by having UEs calculate their gradient Sum-Power levels before the actual gradient transmission occurs. This pre-computation allows the system to determine appropriate channel inversion coefficients in advance, ensuring effective gradient transmission while simplifying the measurement process, as the Sum-Power level is calculated from locally available gradient data rather than requiring complex real-time measurement.
Data Source
AI summary
A method performed by a user equipment (UE) generates local gradients for a federated learning task. The method calculates a gradient Sum-Power level based on the local gradients. The method receives a mapping between gradient Sum-Power levels and scaling factors for channel inversion coefficients to process a data block into an unencoded uplink signal. The method also determines a channel inversion coefficient based on a scaling factor obtained from the mapping and the calculated gradient Sum-Power level. The method applies analog modulation and the channel inversion coefficient to the data block to form the unencoded uplink signal. The method further transmits, to a network, the unencoded uplink signal on shared uplink resources for an over-the-air computation of global gradients for the federated learning task.