Mingkun Yang

Split Federated Learning is a system-efficient federated learning paradigm that leverages the rich computing resources at a central server to train model partitions. Data heterogeneity across silos, however, presents a major challenge undermining the convergence speed and accuracy of the global model. This paper introduces Step-wise Momentum Fusion (SMoFi), an effective and lightweight framework that counteracts gradient divergence arising from data heterogeneity by synchronizing the momentum buffers across server-side optimizers. To control gradient divergence over the training process, we design a staleness-aware alignment mechanism that imposes constraints on gradient updates of the serverside submodel at each optimization step. Extensive validations on multiple real-world datasets show that SMoFi consistently improves global model accuracy (up to 7.1%) and convergence speed (up to 10.25×). Furthermore, SMoFi has a greater impact with more clients involved and deeper learning models, making it particularly suitable for model training in resource-constrained contexts.

Federated Learning (FL) has emerged as a privacy-preserving framework for training models on data generated at the edge. However, the heterogeneity of data silos (e.g., label skew and domain shift) often leads to inconsistent learning objectives and suboptimal model performance. Inspired by the data-driven approach, we propose Flick, a novel data generation framework for heterogeneous FederatedLearning with Commonsense Knowledge from Large Language Models (LLMs). In Flick, the client performs the local data summary to capture client-specific knowledge in textual form. The central server then distills task-relevant, highquality knowledge from the out-of-the-box LLM – guided by cross-client-specific insights – to generate informative text prompts. These prompts direct a generative model in producing synthetic data, enabling global model fine-tuning and local data compensation. This process gradually aligns the label and feature distributions across clients. Extensive results on three datasets demonstrate that Flick improves the global model accuracy by up to 11.43%, and accelerates convergence by up to12.9×, validating its effectiveness in addressing data heterogeneity.

With the widespread application of artificial intelligence, centralized machine learning approaches, which require access to local data, have raised concerns about data privacy. In response, federated learning (FL) has been proposed where models are trained locally on user data and then offloaded to the central server for global aggregation. However, a single global model in FL struggles to meet the diverse personalized needs of clients and suffers significant accuracy degradation when client data distributions are uneven or exhibit non-IID characteristics. Personalized federated learning has been introduced to address data heterogeneity and to meet customized needs. Yet, personalized federated learning (PFL) also has shortcomings: the global model in federated learning often becomes an intermediary product in this framework, lacking the advantage of learning a generalized model. In this paper, we propose FedReG, a new scheme in personalized federated learning with parameter decoupling and rebalanced dataset, to recoup the performance of the global model. By leveraging parameter decoupling and introducing a rebalanced dataset generated according to the distribution of local data, FedReG achieves high accuracy for both the global model and average client models. Extensive experiments also demonstrate the superior scalability of FedReG and robustness over other federated learning and personalized federated learning algorithms.

Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative deep learning model training across distributed data silos. Despite its importance, FL faces challenges such as high latency and less effective global models. In this paper, we propose ShuffleFL, an innovative framework stemming from the hierarchical FL, which introduces a user layer between the FL devices and the FL server. ShuffleFL naturally groups devices based on their affiliations, e.g., belonging to the same user, to ease the strict privacy restriction-"data at the FL devices cannot be shared with others", thereby enabling the exchange of local samples among them. The user layer assumes a multi-faceted role, not just aggregating local updates but also coordinating data shuffling within affiliated devices. We formulate this data shuffling as an optimization problem, detailing our objectives to align local data closely with device computing capabilities and to ensure a more balanced data distribution at the intra-user devices. Through extensive experiments using realistic device profiles and five non-IID datasets, we demonstrate that ShuffleFL can improve inference accuracy by 2.81% to 7.85% and speed up the convergence by 4.11x to 36.56x when reaching the target accuracy.

Federated Learning (FL) is an important privacy-preserving learning paradigm that plays an important role in the Intelligent Internet of Things. Training a global model in FL, however, is vulnerable to the noise in the heterogeneous data across the clients. In this paper, we introduce **FedTrans**, a novel client-transparent client utility estimation method designed to guide client selection for noisy scenarios, mitigating performance degradation problems. To estimate the client utility, we propose a Bayesian framework that models client utility and its relationships with the weight parameters and the performance of local models. We then introduce a variational inference algorithm to effectively infer client utility, given only a small amount of auxiliary data. Our evaluation demonstrates that leveraging FedTrans as a guide for client selection can lead to a better accuracy performance (up to 7.8%), ensuring robustness in noisy scenarios.

Federated Learning (FL) is an important privacy-preserving learning paradigm that is expected to play an essential role in the future Intelligent Internet of Things (IoT). However, model training in FL is vulnerable to noise and the statistical heterogeneity of local data across IoT clients. In this paper, we propose FedNaWi, a “Go Narrow, Then Wide” client selection method that speeds up the FL training, achieves higher model performance, while requiring no additional data or sensitive information transfer from clients. Our method first selects reliable clients (i.e., going narrow) which allows the global model to quickly improve its performance and then includes less reliable clients (i.e., going wide) to exploit more IoT data of clients to further improve the global model. To profile client utility, we introduce a unified Bayesian framework to model the client utility at the FL server, assisted by a small amount of auxiliary data. We conduct extensive evaluations with 5 state-of-the-art FL methods, on 3 IoT tasks and under 7 different types of label and feature noise. We build an FL testbed with 38 IoT nodes (20 nodes run on Raspberry Pi 4B and 18 nodes run on Jetson Nano) for the evaluation. Our results show that FedNaWi improves the FL accuracy substantially and significantly reduces energy consumption. In particular, FedNaWi improves the accuracy from 35% to 75% in the non-IID Dirichlet setting, and reduces the average energy consumption by 55%.

Inertial navigation system (INS) is a practical method for indoor pedestrian navigation without pre-installation of infrastructure. Based on the fundamentals of human bipedal motion, zero velocity update (ZUPT) is a pervasive approach to tackle the accumulated error of inertial measurement units (IMU). While zero velocity detection plays a vital role in the algorithm, existing fixed-threshold methods to pick these pseudo-measurements of error-state Kalman Filter (ESKF) have the doubtful capability to fit various individuals in different motions. To address this issue, we propose the Symmetrical-Net leveraging deep Recurrent Convolutional Neural Networks (RCNNs) to detect the zero velocity interval adaptively. Additionally, two RCNNs are constructed in the symmetrical framework considering bidirectional IMU readings, which further improves the performance of the model. A comprehensive evaluation containing 87 different trajectories from 27 individuals has been conducted. The results show that the detection accuracy is up to 99.5% and 96.5% on the training and validation sets, respectively. It is verified that the precise and robust Symmetrical-Net can be a viable approach for the ZUPT-aided INS system.