Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling

Conceptos Básicos

WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides mobile devices to adaptively select the subnetwork size for local training to accelerate FL training without sacrificing learning accuracy.

Resumen

The paper proposes WHALE-FL, a wireless and heterogeneity aware latency efficient federated learning approach, to accelerate FL training over mobile devices via adaptive subnetwork scheduling.
Key highlights:

Existing fixed-size subnetwork assignment methods are unaware of dynamic changes in device computing/communication conditions and FL training requirements, which can significantly prolong the FL training process.
WHALE-FL designs a novel subnetwork selection utility function to capture system dynamics (device computing and communication conditions) and FL training dynamics (dynamic requirements for local training contributions).
The utility function guides mobile devices to adaptively select the subnetwork size for local training based on their time-varying capabilities and the current stage of FL training.
WHALE-FL prototype-based evaluations show it can significantly accelerate FL training over heterogeneous mobile devices without sacrificing learning accuracy, outperforming peer designs.

Estadísticas

The transmission delay and computing delay for the unit/smallest subnetwork are used to calculate the system efficiency utility.
The average training loss on the local samples is used to calculate the training efficiency utility.

Citas

"WHALE-FL favors system efficiency over training efficiency at the early stage of FL training, and tends to schedule small-sized subnetworks for devices' local training. While FL training steps into the middle stage, if more accurate local training is needed for FL convergence, WHALE-FL prefers training efficiency to system efficiency and schedules to adaptively increase the size of subnetworks for participating mobile devices."
"When FL is close to convergence, WHALE-FL jointly considers system and training efficiencies, and gradually decreases the size of subnetworks for local training, given the fact that most devices have contributed enough to global model and it is unnecessary to keep large-sized subnetworks for local training."

Ideas clave extraídas de

WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling

by Huai-an Su,J... a las arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00885.pdf

Consultas más profundas

How can WHALE-FL be extended to handle more complex system dynamics, such as device mobility and energy constraints

To extend WHALE-FL to handle more complex system dynamics like device mobility and energy constraints, several enhancements can be considered:

Dynamic Subnetwork Adjustment: Incorporate algorithms that can dynamically adjust subnetwork sizes based on real-time changes in device mobility and energy constraints. This would involve continuously monitoring device conditions and adapting the subnetwork sizes accordingly.

Energy-Aware Subnetwork Selection: Develop energy-aware subnetwork selection criteria that take into account the energy levels of devices. Devices with limited energy resources can be assigned smaller subnetworks to conserve energy while still contributing to the FL process.

Mobility Prediction: Implement predictive models that anticipate device mobility patterns. By predicting when devices are likely to move or experience changes in connectivity, the subnetwork scheduling can proactively adjust to ensure minimal disruption to the FL process.

Collaborative Learning: Introduce collaborative learning techniques where devices can share information about their mobility patterns and energy constraints. This collaborative approach can help optimize subnetwork scheduling across multiple devices in a dynamic environment.

What are the potential drawbacks or limitations of the adaptive subnetwork scheduling approach, and how can they be addressed

While adaptive subnetwork scheduling in WHALE-FL offers significant advantages, there are potential drawbacks and limitations that need to be addressed:

Overhead: The adaptive subnetwork selection process may introduce additional computational overhead, especially in scenarios with a large number of devices. This overhead can impact the overall efficiency of the FL process and may need optimization.

Complexity: The adaptive scheduling algorithm's complexity may increase with the incorporation of more dynamic factors. This complexity could make it challenging to implement and maintain, requiring robust testing and validation procedures.

Convergence Speed: Depending on the dynamic changes in system conditions, the adaptive approach may lead to fluctuations in convergence speed. Balancing the trade-offs between system efficiency and training efficiency could impact the overall convergence time.

To address these limitations, it is essential to focus on optimizing the adaptive scheduling algorithm, reducing overhead, and ensuring robustness in handling dynamic system dynamics. Continuous monitoring and fine-tuning of the scheduling process based on performance feedback can help mitigate these drawbacks.

How can the insights from WHALE-FL be applied to other distributed learning paradigms beyond federated learning, such as decentralized learning or edge intelligence

The insights from WHALE-FL can be applied to other distributed learning paradigms beyond federated learning in the following ways:

Decentralized Learning: In decentralized learning settings, where data is distributed across multiple nodes without a central server, adaptive subnetwork scheduling can help optimize model training. By considering the dynamic conditions of each node, such as computing resources and data availability, the scheduling algorithm can improve learning efficiency.

Edge Intelligence: In edge intelligence scenarios, where computation is performed at the network edge, adaptive subnetwork scheduling can enhance model training on edge devices. By adapting subnetwork sizes based on device constraints and network conditions, edge intelligence systems can achieve better performance and energy efficiency.

Collaborative Learning: The collaborative learning aspect of WHALE-FL, where devices adaptively adjust their subnetwork sizes based on system and training dynamics, can be extended to various distributed learning frameworks. This collaborative approach fosters efficient utilization of resources and improved convergence speed in distributed learning environments.