Online Model Updating for Analog Aggregation in Edge Learning
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Solution Overview
Problem
Existing federated learning (FL) in wireless edge networks faces challenges such as communication bottlenecks due to noisy wireless channels and scarcity of radio resources, which lead to accumulated training errors and inefficient power usage, as well as the need for joint optimization of computation and communication that existing algorithms fail to address effectively.
Innovation Solution
The proposed solution is the Online Model Updating for Analog Aggregation (OMUAA) algorithm, which integrates FL with over-the-air (OTA) computation and wireless resource allocation, using current local channel state information to update local models and aggregate them over the air without additional power, while jointly optimizing computation and communication over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional orthogonal multiple access (OMA) is used for model aggregation, then transmission reliability is improved through error-free assumption, but communication latency increases and bandwidth efficiency deteriorates
Solution Approach 1:
The patent merges multiple access channels into a single shared channel, allowing multiple wireless devices to transmit their local models simultaneously through analog aggregation over the air. This combines the transmission operations of multiple devices into a single parallel operation, achieving both reliability and reduced latency by exploiting the superposition property of wireless channels.
Solution Approach 2:
The patent replaces the conventional mechanical sequential transmission approach (OMA) with an analog aggregation approach where models are transmitted simultaneously and aggregated through the wireless channel's superposition property. This substitution eliminates the need for time-division or frequency-division multiplexing, significantly reducing communication latency while maintaining bandwidth efficiency.
2Quantity of substance
If analog aggregation is used to reduce communication overhead, then bandwidth efficiency is improved, but communication errors accumulate due to noisy wireless channels
Solution Approach 1:
The patent introduces feedback mechanisms where the edge server receives analog aggregated models from multiple wireless devices, processes them to obtain updated global models, and feeds these back to the devices for the next iteration. This feedback loop allows the system to compensate for channel noise through multiple iterations and averaging effects, reducing accumulated errors while maintaining high bandwidth efficiency.
Solution Approach 2:
The patent changes the transmission parameter from sequential (OMA) to simultaneous (analog aggregation), and introduces power control parameters to manage transmit power levels. By adjusting power parameters and using analog aggregation, the system achieves bandwidth efficiency while the noisy channel effects are managed through the aggregation process and feedback iterations.
3Ease of manufacture
If separate offline optimization of computation and communication is used, then implementation simplicity is improved, but mutual effects between computation and communication over time are not accounted for
Solution Approach 1:
The patent merges the computation optimization (local model updates) and communication optimization (analog aggregation with power control) into a unified joint optimization framework. This combination allows the system to account for mutual effects between computation and communication over time, improving model training accuracy by considering both aspects simultaneously rather than separately.
4Power
If existing FL algorithms are used with per-iteration power constraints, then short-term power management is improved, but long-term energy usage is not optimized
Solution Approach 1:
The patent introduces dynamic power control mechanisms that adapt transmit power levels based on current channel conditions and optimization needs. This dynamic approach allows the system to manage per-iteration power constraints while also optimizing long-term energy usage by adjusting power allocation across multiple iterations based on feedback and channel state information.
Data Source
AI summary
A method, system and apparatus are disclosed. An edge node configured to communicate with a plurality of wireless devices (WDs) is described. The edge node includes a communication interface configured to receive a plurality of signal vectors from the plurality of WDs, where the plurality of signal vectors is based on a plurality of updated local models associated with the plurality of WDs. The edge node also includes processing circuitry in communication with the communication interface, where the processing circuitry is configured to update a global model based at least on the plurality of signal vectors; and cause at least one transmission of the updated global model to the plurality of WDs.