Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks (1912.13163v1)

Abstract: Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. Rather than sharing, and disclosing, the training dataset with the server, the model parameters (e.g. neural networks weights and biases) are optimized collectively by large populations of interconnected devices, acting as local learners. FL can be applied to power-constrained IoT devices with slow and sporadic connections. In addition, it does not need data to be exported to third parties, preserving privacy. Despite these benefits, a main limit of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters; this has the drawback of a single point of failure and scaling issues for increasing network size. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network by iterating local computations and mutual interactions via consensus-based methods. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing, with intelligence distributed over the end-devices. The proposed methodology is verified by experimental datasets collected inside an industrial IoT environment.

Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks

The paper explores a novel paradigm for federated learning (FL) tailored for massive IoT networks, proposing a fully distributed (server-less) methodology that circumvents traditional server-centric data aggregation. The crux of the investigation lies in optimizing machine learning models across a network of interconnected devices employing consensus-based methods, which facilitates training without requiring data to be stored on a central server. This is instrumental in overcoming significant bottlenecks in existing federated learning setups, such as reliance on centralized architectures and associated scaling issues.

Key Contributions and Methodology

The authors introduce two consensus-based FL algorithms—Consensus-based Federated Averaging (CFA) and Consensus-based Federated Averaging with Gradients Exchange (CFA-GE). These algorithms enable FL in infrastructure-less networks by leveraging device cooperation. The devices, acting as nodes in a peer-to-peer network, perform iterative model updates through distributed consensus approaches rather than relying on a central server, thus reducing single points of failure and enhancing scalability and sustainability of the architecture.

1. CFA Algorithm: The CFA method adopts weighted consensus strategies combined with local stochastic gradient descent (SGD) steps, distributing model updates across a coalition of devices. It allows devices to independently update models using local data batches and next aggregating insights from neighboring nodes.
2. CFA-GE Algorithm: This enhances CFA by exchanging local gradients among nodes, using a novel four-stage negotiation scheme. The enhanced methodology aims to improve convergence speed by exploiting information from neighboring peers' gradients, with momentum-inspired techniques applied to further adapt gradient updates.

Results and Analysis

The paper’s experiments are thorough, executed in a progressive manner across experimental industrial IoT settings, specifically targeting environments where devices such as sub-Thz radars are used for passive movement detection, revealing pertinent applications in human-robot collaborative spaces.

Effectiveness in Real-world Scenarios: Evaluation on industrial IoT environments demonstrates that consensus-based federated learning—particularly the CFA-GE, which integrates both model aggregations and gradient exchanges—achieves comparable performance to traditional centralized machine learning without federation. The proposed methods significantly accelerate convergence while maintaining data privacy, circumventing the requirement to share raw data centrally.

Scalability and Validation: Numerical results highlight scalability with increasing device density and varying network topologies, confirming the flexible application to both convolutional and fully connected neural network architectures. Key insights were provided into tuning hyper-parameters to balance learning rates with communication costs, which is critical for optimizing system performance in massive networks.

Implications and Future Directions

This consensus approach to federated learning holds substantial promise for its potential application across next-generation wireless networks and massive IoT ecosystems. Moving forward, the integration of deep learning with decentralized architectures can lead to enhanced data privacy settings, reduced latency, and increased fault tolerance, necessitating further exploration in areas such as:

• Advanced Model Structures: The adaptation and validation of deeper neural network models that inherently demand more computational power, but potentially offer greater predictive performance.
• Network Variability and Dynamics: Exploration of network dynamics, including time-varying connectivity and environmental conditions, ensuring robustness and adaptability in diverse deployment scenarios.
• Efficiency Schema: Investigation of quantization, compression, and coding techniques to minimize bandwidth usage and maximize throughput in constrained environments.

This paper builds foundational knowledge towards fully decentralized federated learning, proving effective as a scalable alternative to traditional centralized ML approaches, viable for deployment in expansive, heterogeneous IoT landscapes.