5,332 research outputs found
Federated Object Detection for Quality Inspection in Shared Production
Federated learning (FL) has emerged as a promising approach for training
machine learning models on decentralized data without compromising data
privacy. In this paper, we propose a FL algorithm for object detection in
quality inspection tasks using YOLOv5 as the object detection algorithm and
Federated Averaging (FedAvg) as the FL algorithm. We apply this approach to a
manufacturing use-case where multiple factories/clients contribute data for
training a global object detection model while preserving data privacy on a
non-IID dataset. Our experiments demonstrate that our FL approach achieves
better generalization performance on the overall clients' test dataset and
generates improved bounding boxes around the objects compared to models trained
using local clients' datasets. This work showcases the potential of FL for
quality inspection tasks in the manufacturing industry and provides valuable
insights into the performance and feasibility of utilizing YOLOv5 and FedAvg
for federated object detection.Comment: Will submit it to an IEEE conferenc
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
Multimodal data, which can comprehensively perceive and recognize the
physical world, has become an essential path towards general artificial
intelligence. However, multimodal large models trained on public datasets often
underperform in specific industrial domains. This paper proposes a multimodal
federated learning framework that enables multiple enterprises to utilize
private domain data to collaboratively train large models for vertical domains,
achieving intelligent services across scenarios. The authors discuss in-depth
the strategic transformation of federated learning in terms of intelligence
foundation and objectives in the era of big model, as well as the new
challenges faced in heterogeneous data, model aggregation, performance and cost
trade-off, data privacy, and incentive mechanism. The paper elaborates a case
study of leading enterprises contributing multimodal data and expert knowledge
to city safety operation management , including distributed deployment and
efficient coordination of the federated learning platform, technical
innovations on data quality improvement based on large model capabilities and
efficient joint fine-tuning approaches. Preliminary experiments show that
enterprises can enhance and accumulate intelligent capabilities through
multimodal model federated learning, thereby jointly creating an smart city
model that provides high-quality intelligent services covering energy
infrastructure safety, residential community security, and urban operation
management. The established federated learning cooperation ecosystem is
expected to further aggregate industry, academia, and research resources,
realize large models in multiple vertical domains, and promote the large-scale
industrial application of artificial intelligence and cutting-edge research on
multimodal federated learning
Federated Learning on Edge Sensing Devices: A Review
The ability to monitor ambient characteristics, interact with them, and
derive information about the surroundings has been made possible by the rapid
proliferation of edge sensing devices like IoT, mobile, and wearable devices
and their measuring capabilities with integrated sensors. Even though these
devices are small and have less capacity for data storage and processing, they
produce vast amounts of data. Some example application areas where sensor data
is collected and processed include healthcare, environmental (including air
quality and pollution levels), automotive, industrial, aerospace, and
agricultural applications. These enormous volumes of sensing data collected
from the edge devices are analyzed using a variety of Machine Learning (ML) and
Deep Learning (DL) approaches. However, analyzing them on the cloud or a server
presents challenges related to privacy, hardware, and connectivity limitations.
Federated Learning (FL) is emerging as a solution to these problems while
preserving privacy by jointly training a model without sharing raw data. In
this paper, we review the FL strategies from the perspective of edge sensing
devices to get over the limitations of conventional machine learning
techniques. We focus on the key FL principles, software frameworks, and
testbeds. We also explore the current sensor technologies, properties of the
sensing devices and sensing applications where FL is utilized. We conclude with
a discussion on open issues and future research directions on FL for further
studie
Toward porting Astrophysics Visual Analytics Services to the European Open Science Cloud
The European Open Science Cloud (EOSC) aims to create a federated environment
for hosting and processing research data to support science in all disciplines
without geographical boundaries, such that data, software, methods and
publications can be shared as part of an Open Science community of practice.
This work presents the ongoing activities related to the implementation of
visual analytics services, integrated into EOSC, towards addressing the diverse
astrophysics user communities needs. These services rely on visualisation to
manage the data life cycle process under FAIR principles, integrating data
processing for imaging and multidimensional map creation and mosaicing, and
applying machine learning techniques for detection of structures in large scale
multidimensional maps
Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm
Federated learning (FL) has gained significant traction as a
privacy-preserving algorithm, but the underlying resemblances of federated
learning algorithms like Federated averaging (FedAvg) or Federated SGD (Fed
SGD) to ensemble learning algorithms have not been fully explored. The purpose
of this paper is to examine the application of FL to object detection as a
method to enhance generalizability, and to compare its performance against a
centralized training approach for an object detection algorithm. Specifically,
we investigate the performance of a YOLOv5 model trained using FL across
multiple clients and employ a random sampling strategy without replacement, so
each client holds a portion of the same dataset used for centralized training.
Our experimental results showcase the superior efficiency of the FL object
detector's global model in generating accurate bounding boxes for unseen
objects, with the test set being a mixture of objects from two distinct clients
not represented in the training dataset. These findings suggest that FL can be
viewed from an ensemble algorithm perspective, akin to a synergistic blend of
Bagging and Boosting techniques. As a result, FL can be seen not only as a
method to enhance privacy, but also as a method to enhance the performance of a
machine learning model.Comment: 8 pages and submitted to FLTA2023 symposium under IEE
Smart Road Danger Detection and Warning
Road dangers have caused numerous accidents, thus detecting them and warning users are critical to improving traffic safety. However, it is challenging to recognize road dangers from numerous normal data and warn road users due to cluttered real-world backgrounds, ever-changing road danger appearances, high intra-class differences, limited data for one party, and high privacy leakage risk of sensitive information. To address these challenges, in this thesis, three novel road danger detection and warning frameworks are proposed to improve the performance of real-time road danger prediction and notification in challenging real-world environments in four main aspects, i.e., accuracy, latency, communication efficiency, and privacy.
Firstly, many existing road danger detection systems mainly process data on clouds. However, they cannot warn users timely about road dangers due to long distances. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large and precisely labeled datasets to perform well. The EcRD is proposed to improve latency and reduce labeling cost, which is an Edge-cloud-based Road Damage detection and warning framework that leverages the fast-responding advantage of edges and the large storage and computation resources advantages of the cloud. In EcRD, a simple yet efficient road segmentation algorithm is introduced for fast and accurate road area detection by filtering out noisy backgrounds. Additionally, a light-weighted road damage detector is developed based on Gray Level Co-occurrence Matrix (GLCM) features on edges for rapid hazardous road damage detection and warning. Further, a multi-types road damage detection model is proposed for long-term road management on the cloud, embedded with a novel image-label generator based on Cycle-Consistent Adversarial Networks, which automatically generates images with corresponding labels to improve road damage detection accuracy further. EcRD achieves 91.96% accuracy with only 0.0043s latency, which is around 579 times faster than cloud-based approaches without affecting users' experience while requiring very low storage and labeling cost.
Secondly, although EcRD relieves the problem of high latency by edge computing techniques, road users can only achieve warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users' personal information. A novel FedRD named FedRD is developed to improve the coverage range of warning information and protect data privacy. In FedRD, a new hazardous road damage detection model is proposed leveraging the advantages of feature fusion. A novel adaptive federated learning strategy is designed for high-performance model learning from different edges. A new individualized differential privacy approach with pixelization is proposed to protect users' privacy before sharing data. Simulation results show that FedRD achieves similar high detection performance (i.e., 90.32% accuracy) but with more than 1000 times wider coverage than the state-of-the-art, and works well when some edges only have limited samples; besides, it largely preserves users' privacy.
Finally, despite the success of EcRD and FedRD in improving latency and protecting privacy, they are only based on a single modality (i.e., image/video) while nowadays, different modalities data becomes ubiquitous. Also, the communication cost of EcRD and FedRD are very high due to undifferentiated data transmission (both normal and dangerous data) and frequent model exchanges in its federated learning setting, respectively. A novel edge-cloud-based privacy-preserving Federated Multimodal learning framework for Road Danger detection and warning named FedMRD is introduced to leverage the multi-modality data in the real-world and reduce communication costs. In FedMRD, a novel multimodal road danger detection model considering both inter-and intra-class relations is developed. A communication-efficient federated learning strategy is proposed for collaborative model learning from edges with non-iid and imbalanced data. Further, a new multimodal differential privacy technique for high dimensional multimodal data with multiple attributes is introduced to protect data privacy directly on users' devices before uploading to edges. Experimental results demonstrate that FedMRD achieves around 96.42% higher accuracy with only 0.0351s latency and up to 250 times less communication cost compared with the state-of-the-art, and enables collaborative learning from multiple edges with non-iid and imbalanced data in different modalities while preservers users' privacy.2021-11-2
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