Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges (2009.13012v2)

Abstract: The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithms for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Third, we propose two IoT use cases of dispersed federated learning that can offer better privacy preservation than federated learning. Finally, we present several open research challenges with their possible solutions.

Insights into Federated Learning for IoT: Advances and Challenges

Federated Learning (FL) presents a decentralized approach, particularly beneficial for Internet of Things (IoT) environments, where data privacy and system heterogeneity are of paramount importance. The paper by Khan et al. provides an extensive survey of the integration of Federated Learning within IoT frameworks, offering a taxonomy and identifying pivotal challenges in the domain.

Overview

Federated Learning is becoming an attractive solution for IoT due to its capability to process data locally, thus preserving privacy by avoiding the transfer of raw data to central servers. In FL, only model parameters are exchanged, which helps mitigate privacy concerns compared to centralized machine learning approaches. The paper delineates progress in FL for IoT across multiple dimensions, including enhancements in sparsification, robustness, scalability, quantization, security, and privacy.

Key Components of Federated Learning in IoT

1. Distributed Model Training

FL distributes the learning workload across devices, allowing parallel computation. Devices independently compute local models using their private data and share the computed updates with a central server, which aggregates them to update a global model. This approach becomes challenging under non-IID and heterogeneous data distributions typical of IoT networks.

2. Taxonomy of Federated Learning

The paper introduces a taxonomy based on several factors:

• Optimization Schemes: Differentiates between FedAvg and its proximal variants such as FedProx designed to handle system and statistical heterogeneity.
• Aggregation Modes: Highlights centralized versus decentralized and hierarchical aggregation, with implications on robustness and resource utilization.
• Security and Privacy: Discusses mechanisms like differential privacy and homomorphic encryption needed to secure federated learning against malicious attacks.

Evaluation Metrics

The evaluation employs a set of well-defined metrics:

• Security and Privacy: Evaluating the strength against inference attacks by malicious participants or aggregators.
• Scalability: Ability to incorporate numerous devices without performance degradation.
• Quantization: Techniques to reduce communication costs by compressing model updates.
• Robustness: Ensuring system performance despite component failures or attacks.
• Sparsification: Client selection strategies to enhance learning efficiency.

Theoretical and Practical Implications

From a theoretical standpoint, Federated Learning for IoT introduces complexity in model design and algorithmic convergence due to data and system heterogeneity. Practically, applications are vast, ranging from mobile networks to healthcare and vehicular edge computing, as evidenced by various implementations discussed.

Open Challenges and Future Directions

The paper transitions into a discussion of open challenges such as:

• Heterogeneity Management: Addressing both data and system heterogeneities through adaptive algorithms and hybrid aggregation strategies.
• Resource Optimization: Joint optimization of computational and communication resources is critical for sustainability.
• Security Enhancements: Coupling federated learning with robust cryptographic techniques such as homomorphic encryption to maintain privacy without excessive resource overhead.
• Collaborative Learning: Introducing collaboration between edge and cloud infrastructures to enhance scalability and efficiency.

Conclusion

The paper by Khan et al. contributes a comprehensive review of Federated Learning's role in advancing IoT applications, underscoring substantial research opportunities. As IoT ecosystems expand, developing robust, secure, and efficient FL strategies becomes imperative, and this research provides a crucial foundation for ongoing exploration in this rapidly evolving domain.