Asynchronous Federated Learning on Heterogeneous Devices: A Survey (2109.04269v5)

Abstract: Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend. Nonetheless, the synchronous aggregation strategy in the classic FL paradigm, particularly on heterogeneous devices, encounters limitations in resource utilization due to the need to wait for slow devices before aggregation in each training round. Furthermore, the uneven distribution of data across devices (i.e. data heterogeneity) in real-world scenarios adversely impacts the accuracy of the global model. Consequently, many asynchronous FL (AFL) approaches have been introduced across various application contexts to enhance efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing AFL variations using a novel classification scheme, including device heterogeneity, data heterogeneity, privacy, and security on heterogeneous devices, as well as applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated domain.

Asynchronous Federated Learning on Heterogeneous Devices: A Survey

The research paper titled "Asynchronous Federated Learning on Heterogeneous Devices: A Survey" provides a comprehensive analysis of the challenges and existing methodologies related to asynchronous federated learning (AFL) on heterogeneous devices. As federated learning (FL) gains traction for its ability to address privacy concerns by enabling distributed learning across decentralized data sources, AFL emerges as a flexible variant better suited to the diverse computational and communicational capacities of modern edge and IoT devices.

Key Highlights of the Survey

The paper thoroughly analyzes AFL, focusing on major issues such as device heterogeneity, data heterogeneity, and security alongside privacy concerns in distributed settings. It introduces a classification framework to organize existing AFL methodologies and offers insights into possible future research directions.

1. Device Heterogeneity: Traditional synchronous FL struggles with heterogeneous environments mainly due to uneven resource availability and communication bandwidths among devices. Asynchronous FL mitigates synchronization delays by removing the necessity for stragglers to complete updates before aggregation. Nonetheless, this approach presents challenges in balancing resource utilization and model accuracy. Solutions such as node selection strategies, weighted gradient aggregation schemes that account for staleness, and semi-asynchronous and cluster-based FL models are extensively discussed.
2. Data Heterogeneity: AFL inherits challenges in handling non-IID data distributions across devices, potentially skewing global model performance. The paper reviews strategies like introducing constraint terms to account for data variability, using optimized initial parameters, and leveraging clustered FL that groups devices with similar data distributions to alleviate divergence issues.
3. Privacy & Security on Heterogeneous Devices: Integrating blockchain technology into AFL is another distinctive theme. Blockchain's capability to ensure decentralized trust and immutability helps tackle security vulnerabilities like poisoning and Byzantine attacks while safeguarding privacy. The paper discusses several blockchain-empowered approaches, examining the trade-offs between security enhancement and system efficiency.
4. Applications on Heterogeneous Devices: The survey illustrates AFL's applicability across various sectors such as autonomous vehicles, industrial IoT, and mobile edge networks. It underscores AFL’s potential to enhance model training efficiency without compromising on data privacy, paving the way for real-time predictive analytics in dynamic environments.

Implications and Future Directions

The paper identifies promising areas for further development of AFL methodologies. Key research directions include advancing models and algorithms that can dynamically optimize resource allocation and accommodate a wide range of device capabilities. Developing generalized solutions for a broad spectrum of applications and fostering real-world implementations will further validate AFL’s practical utility. The trade-off between privacy protection and model performance remains a critical aspect, as does the need for secure yet scalable blockchain solutions to reinforce the integrity of AFL systems.

In summary, this survey provides valuable insights into AFL's current landscape, highlighting the interplay between technological challenges and methodological innovations. AFL is poised to play a significant role in realizing intelligent, scalable, and privacy-conscious applications across increasingly decentralized and heterogeneous computing environments.