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Register a new userOpenFL: An open-source framework for Federated Learning
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Added by
Sarthak Pati
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.
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The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and security makes each IoT device an isolated island of data. Further, the limited computation and communication capacity of wearable healthcare devices restrict the application of vanilla federated learning. To this end, we propose an advanced federated learning framework to train deep neural networks, where the network is partitioned and allocated to IoT devices and a centralized server. Then most of the training computation is handled by the powerful server. The sparsification of activations and gradients significantly reduces the communication overhead. Empirical study have suggested that the proposed framework guarantees a low accuracy loss, while only requiring 0.2% of the synchronization traffic in vanilla federated learning.
The broad application of artificial intelligence techniques ranging from self-driving vehicles to advanced medical diagnostics afford many benefits. Federated learning is a new breed of artificial intelligence, offering techniques to help bridge the gap between personal data protection and utilization for research and commercial deployment, especially in the use-cases where security and privacy are the key concerns. Here, we present OpenFed, an open-source software framework to simultaneously address the demands for data protection and utilization. In practice, OpenFed enables state-of-the-art model development in low-trust environments despite limited local data availability, which lays the groundwork for sustainable collaborative model development and commercial deployment by alleviating concerns of asset protection. In addition, OpenFed also provides an end-to-end toolkit to facilitate federated learning algorithm development as well as several benchmarks to fair performance comparison under diverse computing paradigms and configurations.
We consider a general class of nonconvex-PL minimax problems in the cross-device federated learning setting. Although nonconvex-PL minimax problems have received a lot of interest in recent years, existing algorithms do not apply to the cross-device federated learning setting which is substantially different from conventional distributed settings and poses new challenges. To bridge this gap, we propose an algorithmic framework named FedSGDA. FedSGDA performs multiple local update steps on a subset of active clients in each round and leverages global gradient estimates to correct the bias in local update directions. By incorporating FedSGDA with two representative global gradient estimators, we obtain two specific algorithms. We establish convergence rates of the proposed algorithms by using novel potential functions. Experimental results on synthetic and real data corroborate our theory and demonstrate the effectiveness of our algorithms.
Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main communication strategies of FL: synchronous FL and asynchronous FL. The advantages of synchronous FL are that the model has high precision and fast convergence speed. However, this synchronous communication strategy has the risk that the central server waits too long for the devices, namely, the straggler effect which has a negative impact on some time-critical applications. Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash. Therefore, we combine the advantages of these two strategies to propose a clustered semi-asynchronous federated learning (CSAFL) framework. We evaluate CSAFL based on four imbalanced federated datasets in a non-IID setting and compare CSAFL to the baseline methods. The experimental results show that CSAFL significantly improves test accuracy by more than +5% on the four datasets compared to TA-FedAvg. In particular, CSAFL improves absolute test accuracy by +34.4% on non-IID FEMNIST compared to TA-FedAvg.
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures. They bring great convenience to the use of GCN, but could hardly apply to the Federated Learning (FL) scenarios with distributed and private datasets, which limit their applications. Moreover, they need to train many candidate GCN models from scratch, which is inefficient for FL. To address these challenges, we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios. FL-AGCNS designs a federated evolutionary optimization strategy to enable distributed agents to cooperatively design powerful GCN models while keeping personal information on local devices. Besides, it applies the GCN SuperNet and a weight sharing strategy to speed up the evaluation of GCN models. Experimental results show that FL-AGCNS can find better GCN models in short time under the FL framework, surpassing the state-of-the-arts NAS methods and GCN models.
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