Analog OTA Gradient Aggregation for Federated Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireless communication systems face challenges in managing and optimizing finite wireless channel resources due to signal attenuation and blocking in complex environments, which undermines established channel measuring and reporting mechanisms.
Innovation Solution
Implementing a base station and user equipment configuration that uses a global federated learning model, where UEs transmit local gradient information to the BS, and the BS aggregates this information in the analog domain using principles of superposition, synchronizing power and timing to reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital domain aggregation is used to aggregate gradient information from multiple UEs, then measurement precision and reliability are improved, but computational complexity and energy consumption at the BS increase significantly
Solution Approach 1:
The patent replaces the digital domain aggregation mechanism (which requires complex digital signal processing and computation at the BS) with an analog domain aggregation mechanism. In the analog domain, gradient information from multiple UEs is aggregated through physical superposition of electrical signals, eliminating the need for complex digital computation, ADC operations for each UE, and reducing BS computational complexity while maintaining aggregation accuracy.
2Device complexity
If analog domain aggregation is used to reduce computational load at BS, then device complexity and energy consumption are reduced, but synchronization requirements become more stringent
Solution Approach 1:
The patent applies preliminary action by having the BS transmit synchronization signals to UEs before the gradient information transmission phase. This allows UEs to synchronize their local clocks and adjust their transmission timing in advance, ensuring that gradient information from multiple UEs arrives at the BS simultaneously or within a acceptable time window, thereby meeting the stringent synchronization requirements for analog domain aggregation.
3Loss of information
If federated learning is implemented with multiple UEs transmitting gradient information, then data privacy is improved through decentralized processing, but wireless channel resource management becomes more difficult due to signal attenuation and blocking
Solution Approach 1:
The patent merges gradient information from multiple UEs into a single aggregated signal through analog domain superposition at the BS. This combining process improves reliability by ensuring that as long as at least one UE successfully transmits its gradient information, the federated learning process can continue. The aggregation of multiple signals provides redundancy against channel attenuation and blocking, maintaining data privacy while overcoming wireless channel reliability issues.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the efficiency of communication and statistical model updates by reducing the computational load at the BS and improving data privacy through decentralized data processing.
Implementation Method 1
transmit, to a plurality of UEs, a first reference signal (RS) via a first transmit beam of the BS
Implementation Method 2
aggregate, in an analog domain, the first set of signals to aggregate the first set of local gradient information received from the multiple UEs
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for over-the-air (OTA) aggregation of data. Certain techniques include transmitting, to a plurality of user equipments (UEs), a first reference signal (RS) via a first transmit beam of a base station (BS), wherein the plurality of UEs and the BS share a global federated learning model: receiving, in response to the first RS, a first set of signals each carrying corresponding local gradient information of a first set of local gradient information for the global federated learning model, the first set of local gradient information comprising local gradient information calculated by each UE of multiple UEs of the plurality of UEs, the first set of local gradient information received via a first receive beam of the BS; and aggregating, in an analog domain, the first set of signals to aggregate the first set of local gradient information received from the multiple UEs.