FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning

Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightforward in/post-training pricing techniques, we study a more difficult problem of pre-training pricing without direct information from the learning process. We propose FLMarket that integrates a two-stage, auction-based pricing mechanism with a security protocol to address the utility-privacy conflict. Through comprehensive experiments, we show that the client selection according to FLMarket can achieve more than 10% higher accuracy in subsequent FL training compared to state-of-the-art methods. In addition, it outperforms the in-training baseline with more than 2% accuracy increase and 3x run-time speedup.

Dataset Distillation-based Hybrid Federated Learning on Non-IID Data

In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-independently and identically distributed (Non-IID) data. This study focuses on the issue of label distribution skew. To address it, we propose a hybrid federated learning framework called HFLDD, which integrates dataset distillation to generate approximately independent and equally distributed (IID) data, thereby improving the performance of model training. Particularly, we partition the clients into heterogeneous clusters, where the data labels among different clients within a cluster are unbalanced while the data labels among different clusters are balanced. The cluster headers collect distilled data from the corresponding cluster members, and conduct model training in collaboration with the server. This training process is like traditional federated learning on IID data, and hence effectively alleviates the impact of Non-IID data on model training. Furthermore, we compare our proposed method with typical baseline methods on public datasets. Experimental results demonstrate that when the data labels are severely imbalanced, the proposed HFLDD outperforms the baseline methods in terms of both test accuracy and communication cost.

Data Augmentation on Graphs: A Survey

In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. For promoting the development of this emerging research direction, in this survey, we comprehensively review and summarize the existing graph data augmentation (GDAug) techniques. Specifically, we first summarize a variety of feasible taxonomies, and then classify existing GDAug studies based on fine-grained graph elements. Furthermore, for each type of GDAug technique, we formalize the general definition, discuss the technical details, and give schematic illustration. In addition, we also summarize common performance metrics and specific design metrics for constructing a GDAug evaluation system. Finally, we summarize the applications of GDAug from both data and model levels, as well as future directions.

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or other factors, making it difficult to learn effective features for HAR tasks, especially when with inadequate data. To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc. Based on a disentanglement network, we designed several loss functions and used an adversarial training strategy for optimization, which can disentangle activity signals from the irrelevant noises with the least dependency (between them) in the feature space. Our BPD framework is flexible, and it can be used on top of existing deep learning (DL) approaches for feature refinement. Extensive experiments were conducted on four public HAR datasets, and the promising results of our proposed BPD scheme suggest its flexibility and effectiveness. This is an open-source project, and the code can be found at http://github.com/Jie-su/BPD

Orchestrating Development Lifecycle of Machine Learning Based IoT Applications: A Survey

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompasses model training and implication involved in holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey on the development lifecycle of ML-based IoT application. We outline core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used in individual stage.