Federated Machine Learning: Concept and Applications
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Explainable Federated Learning for Taxi Travel Time Prediction
Explainable Federated Learning for Taxi Travel Time Prediction Jelena Fiosina a Institute of Informatics, Clausthal Technical University, Julius-Albert Str. 4, D-38678, Clausthal-Zellerfeld, Germany Keywords: FCD Trajectories, Traffic, Travel Time Prediction, Federated Learning, Explainability. Abstract: Transportation data are geographically scattered across different places, detectors, companies, or organisations and cannot be easily integrated under data privacy and related regulations. The federated learning approach helps process these data in a distributed manner, considering privacy concerns. The federated learning archi- tecture is based mainly on deep learning, which is often more accurate than other machine learning models. However, deep-learning-based models are intransparent unexplainable black-box models, which should be explained for both users and developers. Despite the fact that extensive studies have been carried out on investigation of various model explanation methods, not enough solutions for explaining federated models exist. We propose an explainable horizontal federated learning approach, which enables processing of the dis- tributed data while adhering to their privacy, and investigate how state-of-the-art model explanation methods can explain it. We demonstrate this approach for predicting travel time on real-world floating car data from Brunswick, Germany. The proposed approach is general and can be applied for processing data in a federated manner for other prediction and classification tasks. 1 INTRODUCTION transparent. This feature of AI-driven systems com- plicates the user acceptance and can be troublesome Most real-world data are geographically scattered even for model developers. across different places, companies, or organisations, Therefore, the contemporary AI technologies and, unfortunately, cannot be easily integrated under should be capable of processing these data in a de- data privacy and related regulations. -
Exploiting Shared Representations for Personalized Federated Learning
Exploiting Shared Representations for Personalized Federated Learning Liam Collins 1 Hamed Hassani 2 Aryan Mokhtari 1 Sanjay Shakkottai 1 Abstract effective models for each client by leveraging the local com- putational power, memory, and data of all clients (McMahan Deep neural networks have shown the ability to et al., 2017). The task of coordinating between the clients extract universal feature representations from data is fulfilled by a central server that combines the models such as images and text that have been useful received from the clients at each round and broadcasts the for a variety of learning tasks. However, the updated information to them. Importantly, the server and fruits of representation learning have yet to be clients are restricted to methods that satisfy communica- fully-realized in federated settings. Although data tion and privacy constraints, preventing them from directly in federated settings is often non-i.i.d. across applying centralized techniques. clients, the success of centralized deep learning suggests that data often shares a global feature However, one of the most important challenges in federated representation, while the statistical heterogene- learning is the issue of data heterogeneity, where the under- ity across clients or tasks is concentrated in the lying data distribution of client tasks could be substantially labels. Based on this intuition, we propose a different from each other. In such settings, if the server novel federated learning framework and algorithm and clients learn a single shared model (e.g., by minimizing for learning a shared data representation across average loss), the resulting model could perform poorly for clients and unique local heads for each client. -
Architectural Patterns for the Design of Federated Learning Systems
Architectural Patterns for the Design of Federated Learning Systems a,b < a,b a,b b a,b a Sin Kit Lo , , Qinghua Lu , Liming Zhu , Hye-Young Paik , Xiwei Xu and Chen Wang aData61, CSIRO, Australia bUniversity of New South Wales, Australia ARTICLEINFO ABSTRACT Federated learning has received fast-growing interests from academia and industry to tackle the chal- Keywords: lenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system with different components and stakeholders as numerous client de- Federated Learning vices participate in federated learning. Designing a federated learning system requires software system design thinking apart from the machine learning knowledge. Although much effort has been put into Pattern federated learning from the machine learning technique aspects, the software architecture design con- Software Architecture cerns in building federated learning systems have been largely ignored. Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning Machine Learning systems. Architectural patterns present reusable solutions to a commonly occurring problem within a Artificial Intelligence given context during software architecture design. The presented patterns are based on the results of a systematic literature review and include three client management patterns, four model management patterns, three model training patterns, and four model aggregation patterns. The patterns are asso- ciated to the particular state transitions in a federated learning model lifecycle, serving as a guidance for effective use of the patterns in the design of federated learning systems. Central Model 1. Introduction Server aggregation Federated Learning Overview The ever-growing use of big data systems, industrial-scale IoT platforms, and smart devices contribute to the exponen- tial growth in data dimensions [30]. -
A Survey on Federated Learning and Its Applications for Accelerating Industrial Internet of Things
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things Jiehan Zhou Shouhua Zhang Qinghua Lu [email protected] [email protected] [email protected] University of Oulu University of Oulu CSIRO, Data61 13 Garden Street Eveleigh SYDNEY Wenbin Dai Min Chen Xin Liu [email protected] [email protected] [email protected] Shanghai Jiao Tong University Huazhong Univ. of Science China University of and Technology, China Petroleum Huadong Susanna Pirttikangas Yang Shi Weishan Zhang [email protected] [email protected] [email protected] University of Oulu University of Victoria China University of Petroleum Huadong - Qingdao Campus Enrique Herrera-Viedma [email protected] (IoT) is being widely applied in mobile services. There are few Abstract—Federated learning (FL) brings collaborative reports on applying large-scale data and deep learning (DL) to intelligence into industries without centralized training data to implement large-scale enterprise intelligence. One of the accelerate the process of Industry 4.0 on the edge computing level. reasons is lack of machine learning (ML) approaches which can FL solves the dilemma in which enterprises wish to make the use make distributed learning available while not infringing the of data intelligence with security concerns. To accelerate user’s data privacy. Clearly, FL trains a model by enabling the industrial Internet of things with the further leverage of FL, existing achievements on FL are developed from three aspects: 1) individual devices to act as local learners and send local model define terminologies and elaborate a general framework of FL for parameters to a federal server (defined in section 2) instead of accommodating various scenarios; 2) discuss the state-of-the-art training data. -
A Federated Learning Framework for On-Device Anomaly Data Detection
Federated Learning for Internet of Things Tuo Zhang*, Chaoyang He*, Tianhao Ma, Lei Gao, Mark Ma, Salman Avestimehr Univerisity of Southern California Los Angeles, California, USA ABSTRACT anomaly-detection-based intrusion detection in IoT devices. IoTDe- Federated learning can be a promising solution for enabling IoT fender [2] is another similar framework but obtains a personalized cybersecurity (i.e., anomaly detection in the IoT environment) model by fine-tuning the global model trained with FL.[4] evaluates while preserving data privacy and mitigating the high communica- FL-based anomaly detection framework with learning tasks such tion/storage overhead (e.g., high-frequency data from time-series as aggressive driving detection and human activity recognition. [8] sensors) of centralized over-the-cloud approaches. In this paper, to further proposes an attention-based CNN-LSTM model to detect further push forward this direction with a comprehensive study anomalies in an FL manner, and reduces the communication cost in both algorithm and system design, we build FedIoT platform by using Top-: gradient compression. Recently, [15] even evaluates that contains FedDetect algorithm for on-device anomaly data the impact of malicious clients under the setting of FL-based anom- detection and a system design for realistic evaluation of federated aly detection. However, these works do not evaluate the efficacy learning on IoT devices. Furthermore, the proposed FedDetect of FL in a wide range of attack types and lack system design and learning framework improves the performance by utilizing a local performance analysis in a real IoT platform. adaptive optimizer (e.g., Adam) and a cross-round learning rate To further push forward the research in FL-based IoT cybersecu- scheduler. -
Selective Federated Transfer Learning Using Representation Similarity
Selective Federated Transfer Learning using Representation Similarity Tushar Semwal1, Haofan Wang2, Chinnakotla Krishna Teja Reddy3 { 1The University of Edinburgh, 2Carnegie Mellon University, 3GE Healthcare, 1;2;3OpenMined} { [email protected], 2 [email protected], [email protected] } Abstract Transfer Learning (TL) has achieved significant developments in the past few years. However, the majority of work on TL assume implicit access to both the target and source datasets, which limits its application in the context of Federated Learning (FL), where target (client) datasets are usually not directly accessible. In this paper, we address the problem of source model selection in TL for federated scenarios. We propose a simple framework, called Selective Federated Transfer Learning (SFTL), to select the best pre-trained models which provide a positive performance gain when their parameters are transferred on to a new task. We leverage the concepts from representation similarity to compare the similarity of the client model and the source models and provide a method which could be augmented to existing FL algorithms to improve both the communication cost and the accuracy of client models. 1 Introduction The conventional deep learning (DL) approaches involve first collecting data (for instance, images or text) from various sources such as smartphones, and storage devices, and then training a Deep Neural Network (DNN) using this centrally aggregated data. Though this centralised form of architecture is relatively practical to implement and provides full control, the act of gathering data in third-party private servers raises serious privacy concerns. Further, with the Internet of Things (IoT) entering a new era of development, International Data Corporation is estimating a total of 41.6 billion connected IoT devices generating 79.4 zettabytes of data in 2025 [1]. -
Social Information Filtering
Social Information Filtering Hang Li Noah’s Ark Lab Huawei Technologies 1 Talk Outline Huawei Noah’s Ark Lab Our Vision and Approach Social Information Filtering Weibo Robot 2 Huawei Noah’s Ark Lab Mission: ◦ Building “Noah’s Ark” to overcome the deluge of information Research Areas: ◦ Data Mining ◦ Machine Learning ◦ Natural Language Processing ◦ Information Retrieval ◦ Social Media ◦ Mobile Intelligence ◦ Human Computer Interaction Location: Hong Kong, Shenzhen, Beijing Established: June 2012 Our Vision Vision ◦ From Big Data to Deep Knowledge Goal ◦ Building intelligent assistants on mobile devices using big data on the cloud ◦ Passing Turing tests in specific domains 4 Our Approach Big Machine Learning ◦ Acquiring knowledge by large scale machine learning ◦ Example: Google, learning concept of cat from millions of images Human Computation ◦ Humans and machines jointly accomplish tasks ◦ Example: Univ. of Washington, solving large scale gene problem by leveraging human power Lifelong Learning ◦ Continuously accumulating knowledge by machine learning ◦ Example: CMU, NELL (Never Ending Language Learning) 5 Example: Intelligent Mobile Device Video 6 Two Challenges: Information Overload and Information Shortage 7 Social Information Filtering Valuable information is shared on social media Can get information by following key people Taking social media as information filter Building information assistant for each user or each group of users based on the ‘social information filter’ 8 Weibo Robot •Intelligent Information Assistant -
Conference Digest
Conference Digest 2015 IEEE International Conference on Mechatronics and Automation IEEE ICMA 2015 Beijing, China August 2 - 5, 2015 Cosponsored by IEEE Robotics and Automation Society Beijing Institute of Technology Kagawa University, Japan Technically cosponsored by The Institute of Advanced Biomedical Engineering System, BIT Intelligent Robotics Institute, Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, BIT State Key Laboratory of Intelligent Control and Decision of Complex Systems, BIT Tianjin University of Technology Harbin Engineering University Harbin Institute of Technology State Key Laboratory of Robotics and System (HIT) The Robotics Society of Japan The Japan Society of Mechanical Engineers Japan Society for Precision Engineering The Society of Instrument and Control Engineers University of Electro-Communications University of Electronic Science and Technology of China Changchun University of Science and Technology National Natural Science Foundation of China Chinese Mechanical Engineering Society Chinese Association of Automation Life Electronics Society, Chinese Institute of Electronics IEEE ICMA 2015 PROCEEDINGS Additional copies may be ordered from: IEEE Service Center 445 Hoes Lane Piscataway, NJ 08854 U.S.A. IEEE Catalog Number: CFP15839-PRT ISBN: 978-1-4799-7097-1 IEEE Catalog Number (CD-ROM): CFP15839-CDR ISBN (CD-ROM): 978-1-4799-7096-4 Copyright and Reprint Permission: Copyright and Reprint Permission: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. -
Zhuolin Jiang
Zhuolin Jiang CONTACT 50 Moulton St, Cambridge, MA, 02138 Phone: +16178732017 INFORMATION Email: [email protected] Website: http://www.umiacs.umd.edu/~zhuolin/ RESEARCH Computer Vision: Object/Human Detection and Tracking, Object Categorization, Scene INTEREST Modeling and Recognition, Image Tagging/Annotation, Face Detection/Recognition/Verification, Action Detection/Recognition Machine Learning: Discriminative Feature Learning, Supervised Clustering, Submodular Op- timization and Matroid Theory, Sparse and Low-Rank Representation, Deep Learning, On- line/Incremental/Active Learning, Transfer Learning EMPLOYMENT Raytheon BBN Technologies, USA 04/2015 - Present Scientist II Noah’s Ark Research Lab, Huawei, Hong Kong 01/2013 - 03/2015 Researcher University of Maryland, College Park, USA 11/2011 - 12/2012 Assistant Research Scientist (a parallel rank at the Univ. of Maryland to Assistant Professor) University of Maryland, College Park, USA 06/2010 - 10/2011 PostdoctoralResearchAssociate Advisor:Prof.LarryS.Davis EDUCATION University of Maryland, College Park (UMD), USA 09/2008 - 05/2010 Exchange PhD student in Computer Science Advisor: Prof. Larry S. Davis South China University of Technology (SCUT), China 09/2004 - 06/2010 Ph.D. in Computer Science GPA: 3.51/4.0 Advisor: Prof. Shaofa Li Thesis: Research on Computer Vision-based Human Action Detection and Recognition China University of Petroleum (CUP), China 09/2000 - 06/2004 B.Eng. in Computer Science GPA: 3.53/4.0 Rank: 1/113 Advisor: Prof. Faming Gong Thesis: Research on Triangular Mesh Subdivision PUBLICATIONS (Number of Citations: 1790 based on Google Scholar) Jingjing Zheng, Zhuolin Jiang, Rama Chellappa. Submodular Attribute Selection for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (to appear), 2017. -
ASSIST-Iot Technical Report #2
This project has received funding from the European’s Union Horizon 2020 research innovation programme under Grant Agreement No. 957258 Architecture for Scalable, Self-*, human-centric, Intelligent, Secure, and Tactile next generation IoT ASSIST-IoT Technical Report #2 Sunday-FL – Developing Open Source Platform for Federated Learning Piotr Niedziela, Anastasiya Danilenka, Dominik Kolasa, Maria Ganzha, Marcin Paprzycki, Kumar Nalinaksh 2021 Emerging Trends in Industry 4.0 (ETI 4.0) Sunday-FL – Developing Open Source Platform for Federated Learning Piotr Niedziela, Anastasiya Danilenka, Maria Ganzha, Marcin Paprzycki Kumar Nalinaksh Dominik Kolasa Systems Research Institute Institute of Management and Department of Mathematics and Information Science Polish Academy of Sciences Technical Sciences Warsaw University of Technology fi[email protected] Warsaw Management University Abstract—Since its inception, in approximately 2017, federated The latter should be understood as follows. While it is possible learning became an area of intensive research. Obviously, such that computing takes place within a network of workstations, research requires tools that can be used for experimentation. loosely connected using PVM [4], or Condor middleware [5], Here, the biggest industrial players proposed their own platforms, but these platforms are anchored in tools that they “promote”. all nodes havw a single owner and should be treated jointly Moreover, they are mainly “all-in-one” solutions, aimed at as a part of single “virtual computer”. facilitating the federate learning process, rather than supporting Keeping this in mind, let us reflect on changes that took research “about it”. Taking this into account, we have decided place within last few years (and are still ongoing). -
Efficient and Less Centralized Federated Learning
Efficient and Less Centralized Federated Learning Li Chou?, Zichang Liu? ( ), Zhuang Wang, and Anshumali Shrivastava Department of Computer Science Rice University, Houston TX, USA flchou,zl71,zw50,[email protected] Abstract. With the rapid growth in mobile computing, massive amounts of data and computing resources are now located at the edge. To this end, Federated learning (FL) is becoming a widely adopted distributed machine learning (ML) paradigm, which aims to harness this expanding skewed data locally in order to develop rich and informative models. In centralized FL, a collection of devices collaboratively solve a ML task under the coordination of a central server. However, existing FL frame- works make an over-simplistic assumption about network connectivity and ignore the communication bandwidth of the different links in the network. In this paper, we present and study a novel FL algorithm, in which devices mostly collaborate with other devices in a pairwise man- ner. Our nonparametric approach is able to exploit network topology to reduce communication bottlenecks. We evaluate our approach on various FL benchmarks and demonstrate that our method achieves 10× better communication efficiency and around 8% increase in accuracy compared to the centralized approach. Keywords: Machine Learning · Federated Learning · Distributed Sys- tems. 1 Introduction The rapid growth in mobile computing on edge devices, such as smartphones and tablets, has led to a significant increase in the availability of distributed computing resources and data sources. These devices are equipped with ever more powerful sensors, higher computing power, and storage capability, which is contributing to the next wave of massive data in a decentralized manner. -
AAAI-16 / IAAI-16 / EAAI-16 Program
Thirtieth AAAI Conference on Artificial Intelligence Twenty-Eighth Conference on Innovative Applications of Artificial Intelligence The Sixth Symposium on Educational Advances in AI AAAI-16 / IAAI-16 / EAAI-16 Program February 12–17, 2016 Phoenix Convention Center / Hyatt Regency Phoenix Phoenix, Arizona, USA Sponsored by the Association for the Advancement of Artificial Intelligence Cosponsored by AI Journal, the National Science Foundation, Baidu, IBM Research, Infosys, Lionbridge, Microsoft Research, Disney Research, University of Southern California/Information Sciences Institute, Yahoo Labs!, ACM/SIGAI, CRA Computing Community Consortium (CCC), and David E. Smith MORNING AFTERNOON EVENING (AFTER 5:00 PM) Friday, February 12 Tutorial Forum Tutorial Forum Student Welcome Reception Workshops Workshops AAAI/SIGAI DC AAAI/SIGAI DC Saturday, February 13 Tutorial Forum Tutorial Forum Speed Dating Workshops Workshops Opening Reception AAAI/SIGAI DC AAAI/SIGAI DC EAAI EAAI Open House Invited Talks Open House Robotics Exhibits Robotics Exhibits Sunday, February 14 AAAI / IAAI Welcome / AAAI Awards Lunch with a Fellow Presidential Address: Dietterich IAAI Invited Talk: Rao AAAI Invited Talk: Krause AAAI / IAAI Technical Program AAAI / IAAI Technical Program AI Job Fair Classic Paper/Robotics Talks What’s Hot Talks / Robotics Talks Poster / Demo Session 1 EAAI EAAI Fellows Dinner Robotics/Vendor Exhibits Robotics/Vendor Exhibits Video Competition Viewing Video Competition Viewing Monday, February 15 Women’s Mentoring Breakfast AAAI Invited Talk: