Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to privacy, bandwidth limitations, and the prohibitive cost of annotations. Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data is generally either unlabeled or cannot be annotated readily through user interaction. To address these issues, we propose a self-supervised approach termed scalogram-signal correspondence learning based on wavelet transform to learn useful representations from unlabeled sensor inputs as electroencephalography, blood volume pulse, accelerometer, and WiFi channel state information. Our auxiliary task requires a deep temporal neural network to determine if a given pair of a signal and its complementary view (i.e., a scalogram generated with wavelet transform) align with each other or not, by optimizing a contrastive objective. We extensively assess the quality of learned features on diverse public datasets, achieving strong performance in all domains. We evaluate the quality of learned features on downstream tasks through linear classification, effectiveness in the low-data regime, transfer learning, and cross-validation. Our methodology achieves competitive performance with fully-supervised networks, and it works significantly better than pre-training with autoencoders in both central and federated contexts. Moreover, it considerably improves the generalization in a semi-supervised setting by reducing the volume of labeled data required through leveraging self-supervised learning.

Load balancing is directly associated with the overall performance of a parallel and distributed computing system. Although the relevant problems in communication and computation have been well studied in data center environments, few works have considered the issues in an Internet of Things (IoT) edge scenario. In fact, processing data in a load balancing way for the latter case is more challenging. The main reason is that, unlike a data center, both the data sources and the network infrastructure in an IoT edge system can be dynamic. Moreover, with different performance requirements from IoT networks and edge servers, it will be hard to characterize the performance model and to perform runtime optimization for the whole system. To tackle this problem, in this work, we propose a load-balancing aware networking approach for efficient data processing in IoT edge systems. Specifically, we introduce an IoT network dynamic clustering solution using the emerging deep reinforcement learning (DRL), which can both fulfill the communication balancing requirements from IoT networks and the computation balancing requirements from edge servers. Moreover, we implement our system with a long short term memory (LSTM) based Dueling Double Deep Q-Learning Network (D3QN) model, and our experiments with real-world datasets collected from an autopilot vehicle demonstrate that our proposed method can achieve significant performance improvement compared to benchmark solutions.

Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users’ devices, in reality, label noise can naturally occur in FL and is closely related to clients’ characteristics. Due to scarcity of available data and significant label noise variations among clients in FL, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, whereas prior FL studies rely on excessive on-device computational schemes or additional clean data available on the server. We propose FedLN , a framework to deal with label noise across different FL training stages, namely FL initialization, on-device model training, and server model aggregation, able to accommodate the diverse computational capabilities of devices in an FL system. Specifically, FedLN computes per-client noise level estimation in a single federated round and improves the models’ performance by either correcting or mitigating the effect of noisy samples. Our evaluation on various publicly available vision and audio datasets demonstrates a 22% improvement on average compared to other existing methods for a label noise level of 60%. We further validate the efficiency of FedLN in human-annotated real-world noisy datasets and report a 4.8% increase on average in models’ recognition performance, highlighting that FedLN can be useful for improving FL services provided to everyday users.

Detection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user’s context in the daily-life situation a system needs to learn from multimodal data that are often imbalanced, and noisy with missing values. The model is likely to encounter missing sensors in real-life conditions as well (such as a user not wearing a smartwatch) and it fails to infer the context if any of the modalities used for training are missing. In this paper, we propose a method based on an adversarial autoencoder for handling missing sensory features and synthesizing realistic samples. We empirically demonstrate the capability of our method in comparison with classical approaches for filling in missing values on a large-scale activity recognition dataset collected in-the-wild. We develop a fully-connected classification network by extending an encoder and systematically evaluate its multi-label classification performance when several modalities are missing. Furthermore, we show class-conditional artificial data generation and its visual and quantitative analysis on context classification task; representing a strong generative power of adversarial autoencoders.

The Internet of Things (IoT) is opening up new services and is stimulating changes in industries. The lighting industry is also embracing this change by establishing an Internet of Lights (IoL). This article highlights the main benefits and the challenges to face while going towards IoL. To address these challenges and cater to the specific requirements of lighting networks, an IoL reference architecture, Open Architecture for Intelligent Solid State Lighting Systems (OpenAIS), has been proposed. This article provides an overview of the OpenAIS architecture and explains how one can design specific systems based on this architecture. It also zooms into the configurations and design choices made in a pilot system in a real office building showing the validity of the architecture. A comparison of the OpenAIS system with a state-of-the-art commercial solution shows that IoL systems can exceed proprietary systems in several key performance indicators, such as security, interoperability, extensibility and openness.

Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices such as smartphones and virtual assistants, labeling is entrusted to the clients or labels are extracted in an automated way. Specifically, in the case of audio data, acquiring semantic annotations can be prohibitively expensive and time-consuming. As a result, an abundance of audio data remains unlabeled and unexploited on users’ devices. Most existing federated learning approaches focus on supervised learning without harnessing the unlabeled data. In this work, we study the problem of semi-supervised learning of audio models via self-training in conjunction with federated learning. We propose FedSTAR to exploit large-scale on-device unlabeled data to improve the generalization of audio recognition models. We further demonstrate that self-supervised pre-trained models can accelerate the training of on-device models, significantly improving convergence within fewer training rounds. We conduct experiments on diverse public audio classification datasets and investigate the performance of our models under varying percentages of labeled and unlabeled data. Notably, we show that with as little as 3% labeled data available, FedSTAR on average can improve the recognition rate by 13.28% compared to the fully supervised federated model.