Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. However, solving federated machine learning problems raises issues above and beyond those of centralized machine learning. These issues include setting up communication infrastructure between parties, coordinating the learning process, integrating party results, understanding the characteristics of the training data sets of different participating parties, handling data heterogeneity, and operating with the absence of a verification data set.
IBM Federated Learning provides infrastructure and coordination for federated learning. Data scientists can design and run federated learning jobs based on existing, centralized machine learning models and can provide high-level instructions on how to run the federation. The framework applies to both Deep Neural Networks as well as ``traditional'' approaches for the most common machine learning libraries. {\proj} enables data scientists to expand their scope from centralized to federated machine learning, minimizing the learning curve at the outset while also providing the flexibility to deploy to different compute environments and design custom fusion algorithms.
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Arizona State University（亚利桑那州立大学）
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships. In particular, we discuss two directions, namely privacy-preserving graph generation and federated graph learning, which can jointly enable the collaboration among multiple parties each possessing private graph data. For each direction, we identify both "quick wins" and "hard problems". Towards the end, we demonstrate a user interface that can facilitate model exp…
Federated Learning (FL) is an approach to collaboratively train a model across multiple parties without sharing data between parties or an aggregator. It is used both in the consumer domain to protect personal data as well as in enterprise settings, where dealing with data domicile regulation and the pragmatics of data silos are the main drivers. While gradient boosted tree implementations such as XGBoost have been very successful for many use cases, its federated learning adaptations tend to be very slow due to using cryptographic and privacy methods and have not experienced widespread use. W…
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
ENN Group Co.,Ltd.（ENN集团有限公司）；China University of Petroleum（中国石油大学）；Unicom (Shanghai) Industrial Internet Co.,Ltd.（联通（上海）工业互联网有限公司）；Qingdao Windaka Technology Co.,Ltd.（青岛Windaka科技有限公司）
ConsenSys Health；Imperial College London（帝国理工学院）；UC San Diego（加州大学圣地亚哥分校）；Johns Hopkins Berman Institute of Bioethics（约翰斯·霍普金斯大学伯曼生物伦理研究所）
We propose a novel architecture for federated learning within healthcare consortia. At the heart of the solution is a unique integration of privacy preserving technologies, built upon native enterprise blockchain components available in the Ethereum ecosystem. We show how the specific characteristics and challenges of healthcare consortia informed our design choices, notably the conception of a new Secure Aggregation protocol assembled with a protected hardware component and an encryption toolkit native to Ethereum. Our architecture also brings in a privacy preserving audit trail that logs eve…
Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smar…
HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric
atlanTTic Research Center, Information & Computing Lab (ICLAB)（atlanTTic研究所以及信息与计算实验室）；Telecommunication Engineering School, Universidade de Vigo（电信工程学院，乌维戈大学）；CUD Escuela Naval Militar Universidade de Vigo / Escuela Naval Militar（CUD海军学院，乌维戈大学 / 海军学院）
For the modern world where data is becoming one of the most valuable assets, robust data privacy policies rooted in the fundamental infrastructure of networks and applications are becoming an even bigger necessity to secure sensitive user data. In due course with the ever-evolving nature of newer statistical techniques infringing user privacy, machine learning models with algorithms built with respect for user privacy can offer a dynamically adaptive solution to preserve user privacy against the exponentially increasing multidimensional relationships that datasets create. Using these privacy a…
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on the edge computing level. FL solves the dilemma in which enterprises wish to make the use of data intelligence with security concerns. To accelerate industrial Internet of things with the further leverage of FL, existing achievements on FL are developed from three aspects: 1) define terminologies and elaborate a general framework of FL for accommodating various scenarios; 2) discuss the state-of-the-art of FL on fundamental researches includin…
Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with privacy protection. In the training phase, VFL only exchanges the intermediate statistics, i.e., forward activations and backward derivatives, across parties to compute model gradients. Nevertheless, due to its geo-distributed nature, VFL training usually suffers from the low WAN bandwidth. In this paper, we introduce CELU-VFL, a novel and efficient VFL training framework that exploits the local update technique to re…
Principles and Components of Federated Learning Architectures
Research and Development Department（研发部）；University of Hertfordshire（赫特福德大学）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Westcliff university（韦斯特克莱夫大学）；Clark Atlanta University（克拉克亚特兰大大学）
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
Cooperative Innovation Center, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所协同创新中心）；University of Chinese Academy of Sciences（中国科学院大学）；JD Technology Group（京东科技集团）；University of Technology Sydney（悉尼科技大学）
University of Murcia（穆尔西亚大学）；European Commission, Joint Research Centre（欧盟委员会联合研究中心）
The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through an effective and efficient detection process. In the context of the Internet of Things (IoT), most ML-enabled IDS approaches use centralized approaches where IoT devices share their data with data centers for further analysis. To mitigate privacy concerns associated with centralized approaches, in recent years the use of Federated Learning (FL) has attracted a significant interest in different sectors, including hea…
Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers
Mathematics and Computer Science Division, Argonne National Laboratory（阿贡国家实验室数学与计算机科学 division）；Data Science and Learning Division, Argonne National Laboratory（阿贡国家实验室数据科学与学习 division）；Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）
More data is almost always beneficial for analysis and machine learning tasks. In many realistic situations however, an enterprise cannot share its data, either to keep a competitive advantage or to protect the privacy of the data sources, the enterprise's clients for example. We propose a method for data owners to share synthetic or fake versions of their data without sharing the actual data, nor the parameters of models that have direct access to the data. The method proposed is based on the privGAN architecture where local GANs are trained on their respective data subsets with an extra pena…
Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision-making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model t…
Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Center for Applied Statistics, School of Statistics, Renmin University of China（中国人民大学统计学院应用统计中心）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Department of Statistics, Fudan University（复旦大学统计系）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
