Federated Learning Over Wireless in WLANs
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Solution Overview
Problem
Federated learning in wireless local area networks (WLANs) faces challenges in efficiently sharing machine learning models and gradients among distributed users, leading to excessive overhead and communication burdens, while also requiring power-saving mechanisms to manage client activity.
Innovation Solution
The implementation of an Enhanced Broadcast Service (EBCS) framework enables efficient sharing of machine learning model parameters, such as gradients, through broadcast, multicast, or unicast messages, with operational parameter setup and synchronization between access points (APs) and stations (STAs), allowing for reduced overhead and power conservation by activating STAs only when needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If federated learning is implemented in WLANs using traditional communication methods, then distributed machine learning can be achieved, but excessive communication overhead and network burden occur
Solution Approach 1:
The patent combines federated learning communication with existing WLAN broadcast service infrastructure, merging model parameter transmission with regular broadcast traffic. This integration allows FL gradients and model updates to be transmitted using the same broadcast mechanism already present in WLANs, thereby reducing additional communication overhead and network burden.
Solution Approach 2:
The broadcast service in WLANs is designed to serve multiple purposes: traditional broadcast traffic and federated learning model synchronization. By making the broadcast mechanism universal, the system can handle both conventional data broadcasting and FL parameter distribution through the same channel, eliminating the need for dedicated FL communication resources.
2Speed
If all client STAs are kept active continuously to participate in FL, then fast model convergence can be achieved, but excessive power consumption occurs
Solution Approach 1:
The patent implements periodic broadcast announcements from the AP to notify STAs about FL training rounds. STAs can remain in low-power sleep mode between announcements and only wake up when notified to participate in FL activities. This periodic notification mechanism enables fast convergence when needed while allowing power saving during idle periods.
Solution Approach 2:
The system allows STAs to autonomously manage their power states based on broadcast notifications. When an FL training round is initiated, the AP broadcasts a notification, and STAs self-activate to participate. When no training is needed, STAs self-deactivate to save power, making the power management autonomous and efficient.
3Reliability
If FL operational parameters are synchronized between AP and STAs, then coordinated FL training can be achieved, but increased protocol complexity occurs
Solution Approach 1:
The patent establishes FL operational parameters (such as learning rate, batch size, number of rounds) in advance during an initialization phase before actual training begins. The AP configures these parameters and broadcasts them to all participating STAs beforehand, ensuring all devices are synchronized and ready for coordinated training without needing complex real-time negotiation protocols.
Data Source
AI summary
A federated learning (FL) and/or a distributed machine learning (ML) model sharing process may be implemented in a wireless network. An access point (AP), station (STA), or the like may provide a FL announcement message indicating that a FL or ML process is in being utilized. A STA receiving the announcement message may provide a FL/ML support frame indicating its participation in FL model sharing. The STA may comprise a local FL model executable on the STA. The STA may update its local FL model based on information in the FL announcement message. The STA may update its local FL model based on information received from other STAs in the network. The FL announcement message may comprise a schedule, and the STA may be configured to train its local FL model in accordance with the schedule. The sharing model process may be implemented in a wireless local area network.