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1. Participant Selection for Hierarchical Federated Learning in Edge Clouds

   https://doi.org/10.1109/NAS55553.2022.9925313  

   Wei, Xinliang; Liu, Jiyao; Shi, Xinghua; Wang, Yu (October 2022, 2022 IEEE International Conference on Networking, Architecture and Storage (NAS))

   Federated learning (FL) has been emerging as a new distributed machine learning paradigm recently. Although FL can protect the data privacy of participants by keeping their training data on local devices, there are recent works raising new privacy concerns especially when workers or the parameter server of FL are untrustworthy or malicious. One effective way to solve the problem is using hierarchical federated learning (HFL) where a few middle-layer aggregators (or called group leaders) are used to aggregate local model updates from workers and send group model updates to the parameter server. In this paper, we consider the participant selection problem of HFL in an edge cloud with multiple FL models, where each model needs to select one parameter server, a few group leaders and a certain amount of workers from edge servers to jointly perform HFL. We first formulate this problem as a non-linear integer programming, aiming to minimize the total learning cost of all models while satisfying the constrained edge resources. We then design a three-stage algorithm by decoupling the original problem into three sub-problems and solving them iteratively. Simulations with real-world datasets and FL models confirm that our proposed algorithm can efficiently reduce the average total learning cost in edge cloud compared with existing methods. 

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2. Memristor Based Federated Learning for Network Security on the Edge using Processing in Memory (PIM) Computing

   https://doi.org/10.1109/IJCNN55064.2022.9891986  

   Alam, Shahanur; Yakopcic, Chris; Taha, Tarek M. (July 2022, 2022 International Joint Conference on Neural Networks (IJCNN))

   Artificial Intelligence (AI) is moving towards the edge. Training an AI model for edge computing on a centralized server increases latency, and the privacy of edge users is jeopardized due to private data transfer through a less secure communication channels. Additionally, existing high-power computing systems are battling with memory and data transfer bottlenecks between the processor and memory. Federated Learning (FL) is a collaborative AI learning paradigm for distributed local devices that operates without transferring local data. Local participant devices share the updated network parameters with the central server instead of sending the original data. The central server updates the global AI model and deploys the model to the local clients. As the local data resides only on the edge, these devices need to be protected from cyberattacks. The Federated Intrusion Detection System (FIDS) could be a viable system to protect edge devices as opposed to a centralized protection system. However, on-device training of the model in resource constrained devices may suffer from excessive power drain, in addition to memory and area overhead. In this work we present a memristor based system for AI training on edge devices. Memristor devices are ideal candidates for processing in memory, as their dynamic resistance properties allow them to perform multiply-add operations in parallel in the analog domain with extreme efficiency. Alternatively, existing CMOS-based PIM systems are typically developed for edge inference based on pretrained weights, and are not equipped for on-chip training. We show the effectiveness of the system, where successful learning and recognition is achieved completely within edge devices. The classification accuracy of the memristor system shows negligible loss when compared a software implementation. To the best of our knowledge, this first demonstration of a memristor based federated learning system. We demonstrate the effectiveness of this system as an intrusion detection platform for edge devices, although given the flexibility of the learning algorithm, it could be used to enhance many types of on board leaning and classification applications. 

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3. Hybrid Local SGD for Federated Learning with Heterogeneous Communications

   Guo, Yuanxiong; Sun, Ying; Hu, Rui; Gong, Yanmin (January 2022, International Conference on Learning Representations)

   Communication is a key bottleneck in federated learning where a large number of edge devices collaboratively learn a model under the orchestration of a central server without sharing their own training data. While local SGD has been proposed to reduce the number of FL rounds and become the algorithm of choice for FL, its total communication cost is still prohibitive when each device needs to communicate with the remote server repeatedly for many times over bandwidth-limited networks. In light of both device-to-device (D2D) and device-to-server (D2S) cooperation opportunities in modern communication networks, this paper proposes a new federated optimization algorithm dubbed hybrid local SGD (HL-SGD) in FL settings where devices are grouped into a set of disjoint clusters with high D2D communication bandwidth. HL-SGD subsumes previous proposed algorithms such as local SGD and gossip SGD and enables us to strike the best balance between model accuracy and runtime. We analyze the convergence of HL-SGD in the presence of heterogeneous data for general nonconvex settings. We also perform extensive experiments and show that the use of hybrid model aggregation via D2D and D2S communications in HL-SGD can largely speed up the training time of federated learning. 

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4. Peer-to-Peer Deep Learning for Beyond-5G IoT

   https://doi.org/10.1109/IEEECONF60004.2024.10943006  

   Pranav, Srinivasa; Moura, José_M F (October 2024, IEEE)

   We present P2PL, a practical multi-device peer-to-peer deep learning algorithm that, unlike the federated learning paradigm, does not require coordination from edge servers or the cloud. This makes P2PL well-suited for the sheer scale of beyond-5G computing environments like smart cities that otherwise create range, latency, bandwidth, and single point of failure issues for federated approaches. P2PL introduces max norm synchronization to catalyze training, preserves on-device deep model training to preserve privacy, and leverages local inter-device communication to implement distributed consensus. Each device iteratively alternates between two phases: 1) on-device learning and 2) peer-to-peer cooperation where they combine model parameters with nearby devices. We empirically show that all participating devices achieve the same test performance attained by federated and centralized training – even with 100 devices and relaxed singly stochastic consensus weights. We extend results to experimental settings with diverse network topologies, sparse and intermittent communication, and non-IID data distributions. 

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5. Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning

   https://doi.org/10.1109/TVT.2025.3614630  

   Zhang, Yu; Gao, Zhidong; Zhang, Zhenxiao; Wang, Tongnian; Gong, Yanmin; Guo, Yuanxiong (September 2025, IEEE Transactions on Vehicular Technology)

   Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-preserving advantages, FL has been widely adopted across various real-world applications. However, deploying FL over resource-constrained mobile networks presents challenges, including high training latency and reduced model accuracy, particularly in the presence of data and system heterogeneity. Additionally, mobile networks are affected by communication dynamics, further complicating efficient FL deployment. To address these challenges, this paper investigates optimal client scheduling and resource allocation for FL in resource-constrained, dynamic mobile networks. Specifically, we first analyze how client sampling probability affects model convergence in FL and formulate a stochastic optimization problem to minimize training latency and convergence bound while satisfying the energy constraints. To solve the formulated problem, we propose an online control algorithm, dubbed Lyapunov-based Resource-efficient Online Algorithm (LROA), which operates without requiring the knowledge of future communication dynamics. Extensive experimental results show that the proposed scheme significantly reduces training latency and improves resource efficiency compared to various baselines. 

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