Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework (2002.10671v3)

Abstract: Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneity in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.

Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework

The paper "Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework" presents a detailed investigation into the application of federated learning within the context of the Internet of Things (IoT) environments. Through this work, the authors propose a new framework named PerFit that leverages personalized federated learning alongside cloud-edge computing to address inherent heterogeneity challenges in IoT applications.

Challenges in Federated Learning for IoT

The paper accurately identifies three primary challenges faced by traditional federated learning in IoT environments:

1. Device Heterogeneity: IoT devices vary significantly in their computational, storage, and communication capabilities, often operating under constrained resources and intermittent network conditions. This results in high communication costs, computation delays, and fault tolerance issues that traditional federated learning does not handle adequately.
2. Statistical Heterogeneity: The non-IID nature of data collected from various IoT devices poses a significant challenge. Traditional federated learning algorithms like FedAvg often suffer degraded performance because they cannot fully leverage the specific characteristics of each device's data.
3. Model Heterogeneity: Different IoT devices may require model architectures tailored to their specific resources and applications. Traditional federated models assume homogeneity in model architecture across clients, which is impractical in realistic applications.

PerFit Framework

The PerFit framework is introduced as a comprehensive solution that applies personalized federated learning in cloud-edge architectures to address the aforementioned challenges:

• It utilizes edge computing to reduce latency and communication costs, enabling real-time processing and offloading computational burdens from resource-constrained devices.
• PerFit facilitates the development of personalized models for each edge device, acknowledging the specialized data and application requirements unique to each IoT node.
• The framework allows for the integration of several personalized learning approaches, including federated transfer learning, federated meta-learning, federated multi-task learning, and federated distillation, each targeting specific facets of the heterogeneity issue.

Personalized Federated Learning Approaches

The paper further discusses various methods employed within the PerFit framework to enable personalization:

• Federated Transfer Learning: This method allows IoT devices to fine-tune a globally trained model with local data, effectively addressing statistical heterogeneity while optimizing communication by only transmitting necessary model parameters.
• Federated Meta-Learning: By leveraging gradient-based meta-learning techniques, this approach enhances the adaptability of models trained on non-IID data, proving beneficial for devices with limited local data.
• Federated Multi-Task Learning: Through the learning of task relationships across devices, this method yields personalized models suited to differing tasks and reduces communication overhead.
• Federated Distillation: This strategy facilitates model heterogeneity by using knowledge distillation, enabling different devices to maintain unique model architectures while benefiting from collaborative training.

Evaluation and Case Study

The authors conducted experiments with their framework on a human activity recognition task using the MobiAct dataset. Their findings corroborated that personalized federated learning approaches within the PerFit framework enhance model accuracy and reduce communication payload compared to traditional federated learning and centralized learning setups. Notably, the federated transfer learning approach achieved superior accuracy with lower communication requirements, highlighting its suitability for IoT scenarios with resource constraints.

Implications and Future Directions

This work provides a substantial contribution to the field of distributed machine learning, particularly in addressing the challenges posed by IoT environments. The proposed PerFit framework not only underscores the potential of personalized federated learning in deploying intelligent IoT applications but also paves the path for future research into more efficient means of handling heterogeneity at multiple levels.

Looking forward, further development may involve the integration of advanced privacy-preserving techniques, exploring scalability with larger IoT ecosystems, and extending applications to other domains such as healthcare and smart infrastructure. By refining these methodologies, the PerFit framework could greatly enhance the feasibility and performance of large-scale IoT deployments, ensuring robust and efficient AI-driven insights and functionalities across diverse applications.