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Tianjian Chen

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14 papers

2 author rows

JBHI Journal 2026 Journal Article

A Multi-Scale Attention-based Reconstruction Fusion Network for Motor Imagery Classification

Lina Qiu
You Hu
Minjin Wu
Baiqiang Long
Tianjian Chen
Jiahui Pan

Motor imagery (MI) is a widely used cognitive paradigm in brain-computer interface (BCI) systems, where accurate and efficient MI decoding is essential for real-time human-machine interaction. However, the non-stationary nature and pronounced inter-subject variability of electroencephalography (EEG) signals pose significant challenges to reliable decoding. To address these issues, we propose a multi-scale attention-based reconstruction fusion network (MSARFNet) for MI-EEG decoding. The proposed framework employs parallel multi-scale convolutional branches to extract discriminative spatio-temporal features at different temporal resolutions. An attention-based reconstruction fusion module is then introduced to selectively diminish non-dominant information while promoting effective interaction among multi-scale features. Furthermore, a local-global temporal encoding strategy is designed to enhance transient MI-related responses through local temporal context aggregation and subsequently capture long-range temporal dependencies via global temporal modeling. Subject-dependent experiments conducted on the BCI Competition IV 2a and 2b datasets demonstrate that MSARFNet achieves average classification accuracies of 84. 64% and 87. 96%, respectively, outperforming several state-of-the-art methods. These results indicate that MSARFNet provides an effective and robust solution for EEG-based MI decoding.

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ICRA Conference 2021 Conference Paper

Design Paradigms Based on Spring Agonists for Underactuated Robot Hands: Concepts and Application

Tianjian Chen
Tianyi Zhang
Matei Ciocarlie

In this paper, we focus on a rarely used paradigm in the design of underactuated robot hands: the use of springs as agonists and tendons as antagonists. We formalize this approach in a design matrix also considering its interplay with the underactuation method used (one tendon for multiple joints vs. multiple tendons on one motor shaft). We then show how different cells in this design matrix can be combined in order to facilitate the implementation of desired postural synergies with a single motor. Furthermore, we show that when agonist and antagonist tendons are combined on the same motor shaft, the resulting spring force cancellation can be leveraged to produce multiple desirable behaviors, which we demonstrate in a physical prototype.

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JMLR Journal 2021 Journal Article

FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection

Yang Liu
Tao Fan
Tianjian Chen
Qian Xu
Qiang Yang

Collaborative and federated learning has become an emerging solution to many industrial applications where data values from different sites are exploit jointly with privacy protection. We introduce FATE, an industrial-grade project that supports enterprises and institutions to build machine learning models collaboratively at large-scale in a distributed manner. FATE supports a variety of secure computation protocols and machine learning algorithms, and features out-of-box usability with end-to-end building modules and visualization tools. Documentations are available at https://github.com/FederatedAI/FATE. Case studies and other information are available at https://www.fedai.org. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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IS Journal 2021 Journal Article

SecureBoost: A Lossless Federated Learning Framework

Kewei Cheng
Tao Fan
Yilun Jin
Yang Liu
Tianjian Chen
Dimitrios Papadopoulos
Qiang Yang

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy. To meet this goal, in this article, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy. This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned dataset. An advantage of SecureBoost is that it provides the same level of accuracy as the non -privacy-preserving approach while at the same time, reveals no information of each private data provider. We show that the SecureBoost framework is as accurate as other nonfederated gradient tree-boosting algorithms that require centralized data, and thus, it is highly scalable and practical for industrial applications such as credit risk analysis. To this end, we discuss information leakage during the protocol execution and propose ways to provably reduce it.

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TIST Journal 2021 Journal Article

StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing

Anbu Huang
Yang Liu
Tianjian Chen
Yongkai Zhou
Quan Sun
Hongfeng Chai
Qiang Yang

From facial recognition to autonomous driving, Artificial Intelligence (AI) will transform the way we live and work over the next couple of decades. Existing AI approaches for urban computing suffer from various challenges, including dealing with synchronization and processing of vast amount of data generated from the edge devices, as well as the privacy and security of individual users, including their bio-metrics, locations, and itineraries. Traditional centralized-based approaches require data in each organization be uploaded to the central database, which may be prohibited by data protection acts, such as GDPR and CCPA. To decouple model training from the need to store the data in the cloud, a new training paradigm called Federated Learning (FL) is proposed. FL enables multiple devices to collaboratively learn a shared model while keeping the training data on devices locally, which can significantly mitigate privacy leakage risk. However, under urban computing scenarios, data are often communication-heavy, high-frequent, and asynchronized, posing new challenges to FL implementation. To handle these challenges, we propose a new hybrid federated learning architecture called StarFL. By combining with Trusted Execution Environment (TEE), Secure Multi-Party Computation (MPC), and (Beidou) satellites, StarFL enables safe key distribution, encryption, and decryption, and provides a verification mechanism for each participant to ensure the security of the local data. In addition, StarFL can provide accurate timestamp matching to facilitate synchronization of multiple clients. All these improvements make StarFL more applicable to the security-sensitive scenarios for the next generation of urban computing.

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IS Journal 2020 Journal Article

A Secure Federated Transfer Learning Framework

Yang Liu
Yan Kang
Chaoping Xing
Tianjian Chen
Qiang Yang

Machine learning relies on the availability of vast amounts of data for training. However, in reality, data are mostly scattered across different organizations and cannot be easily integrated due to many legal and practical constraints. To address this important challenge in the field of machine learning, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical modeling under a data federation. FTL allows knowledge to be shared without compromising user privacy and enables complementary knowledge to be transferred across domains in a data federation, thereby enabling a target-domain party to build flexible and effective models by leveraging rich labels from a source domain. This framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the nonprivacy-preserving transfer learning. It is flexible and can be effectively adapted to various secure multiparty machine learning tasks.

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IS Journal 2020 Journal Article

A Sustainable Incentive Scheme for Federated Learning

Han Yu
Zelei Liu
Yang Liu
Tianjian Chen
Mingshu Cong
Xi Weng
Dusit Niyato
Qiang Yang

In federated learning (FL), a federation distributedly trains a collective machine learning model by leveraging privacy preserving technologies. However, FL participants need to incur some cost for contributing to the FL models. The training and commercialization of the models will take time. Thus, there will be delays before the federation could pay back the participants. This temporary mismatch between contributions and rewards has not been accounted for by existing payoff-sharing schemes. To address this limitation, we propose the FL incentivizer (FLI). It dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff received and the waiting time for receiving payoffs. Comparisons with five state-of-the-art payoff-sharing schemes show that FLI attracts high-quality data owners and achieves the highest expected revenue for a federation.

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ECAI Conference 2020 Conference Paper

RPN: A Residual Pooling Network for Efficient Federated Learning

Anbu Huang
Yuanyuan Chen 0012
Yang Liu 0165
Tianjian Chen
Qiang Yang 0001

Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. Due to model complexity, network unreliability and connection in-stability, communication cost has became a major bottleneck for applying federated learning to real-world applications. Current existing strategies are either need to manual setting for hyperparameters, or break up the original process into multiple steps, which make it hard to realize end-to-end implementation. In this paper, we propose a novel compression strategy called Residual Pooling Network (RPN). Our experiments show that RPN not only reduce data transmission effectively, but also achieve almost the same performance as compared to standard federated learning. Our new approach performs as an end-to-end procedure, which should be readily applied to all CNN-based model training scenarios for improvement of communication efficiency, And hence make it easy to deploy in real-world application without much human intervention.

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IJCAI Conference 2019 Conference Paper

Fair and Explainable Dynamic Engagement of Crowd Workers

Han Yu
Yang Liu
Xiguang Wei
Chuyu Zheng
Tianjian Chen
Qiang Yang
Xiong Peng

Years of rural-urban migration has resulted in a significant population in China seeking ad-hoc work in large urban centres. At the same time, many businesses face large fluctuations in demand for manpower and require more efficient ways to satisfy such demands. This paper outlines AlgoCrowd, an artificial intelligence (AI)-empowered algorithmic crowdsourcing platform. Equipped with an efficient explainable task-worker matching optimization approach designed to focus on fair treatment of workers while maximizing collective utility, the platform provides explainable task recommendations to workers' personal work management mobile apps which are becoming popular, with the aim to address the above societal challenge.

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TIST Journal 2019 Journal Article

Federated Machine Learning

Qiang Yang
Yang Liu
Tianjian Chen
Yongxin Tong

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

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IJCAI Conference 2019 Conference Paper

Multi-Agent Visualization for Explaining Federated Learning

Xiguang Wei
Quan Li
Yang Liu
Han Yu
Tianjian Chen
Qiang Yang

As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.

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IROS Conference 2018 Conference Paper

Proprioception-Based Grasping for Unknown Objects Using a Series-Elastic-Actuated Gripper

Tianjian Chen
Matei Ciocarlie

Grasping unknown objects has been an active research topic for decades. Approaches range from using various sensors (e. g. vision, tactile) to gain information about the object, to building passively compliant hands that react appropriately to contacts. In this paper, we focus on grasping unknown objects using proprioception (the combination of joint position and torque sensing). Our hypothesis is that proprioception alone can be the basis for versatile performance, including multiple types of grasps for objects with multiple shapes and sizes, and transitions between grasps. Using a series-elastic-actuated gripper, we propose a method for performing stable fingertip grasps for unknown objects with unknown contacts, formulated as multi-input-multi-output (MIMO) control. We also show that the proprioceptive gripper can perform enveloping grasps, as well as the transition from fingertip grasps to enveloping grasps.

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ICRA Conference 2018 Conference Paper

Underactuated Hand Design Using Mechanically Realizable Manifolds

Tianjian Chen
Maximilian Haas-Heger
Matei Ciocarlie

Hand synergies, or joint coordination patterns, have become an effective tool for achieving versatile robotic grasping with simple hands or planning algorithms. Here we propose a method to determine the hand synergies such that they can be physically implemented in an underactuated fashion. Given a kinematic hand model and a set of desired grasps, our algorithm optimizes a Mechanically Realizable Manifold designed to be achievable by a physical underactuation mechanism, enabling the resulting hand to achieve the desired grasps with few actuators. Furthermore, in contrast to existing methods for determining synergies which are only concerned with hand posture, our method explicitly optimizes the stability of the target grasps. We implement this method in the design of a three-finger single-actuator hand as an example, and evaluate its effectiveness numerically and experimentally.

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ICRA Conference 2015 Conference Paper

An ankle-foot prosthesis emulator with control of plantarflexion and inversion-eversion torque

Steven H. Collins
Myunghee Kim
Tianjian Chen
Tianyao Chen

Ankle inversion-eversion compliance is an important feature of conventional prosthetic feet, and control of inversion, or roll, in robotic prostheses could improve balance for people with amputation. We designed a tethered ankle-foot prosthesis with two independently-actuated toes that are coordinated to provide plantarflexion and inversion-eversion torques. This configuration allows a simple lightweight structure with a total mass of 0. 72 kg. Strain gages on the toes measure torque with less than 2. 7% RMS error, while compliance in the Bowden cable tether provides series elasticity. Benchtop tests demonstrated a 90% rise time of less than 33 ms and peak torques of 180 N·m in plantarflexion and ±30 N·m in inversion-eversion. The phase-limited closedloop torque bandwidth is 20 Hz with a 90 N·m amplitude chirp in plantarflexion, and 24 Hz with a 20 N·m amplitude chirp in inversion-eversion. The system has low sensitivity to toe position disturbances at frequencies of up to 18 Hz. Walking trials with five values of constant inversion-eversion torque demonstrated RMS torque tracking errors of less than 3. 7% in plantarflexion and less than 5. 9% in inversion-eversion. These properties make the platform suitable for haptic rendering of virtual devices in experiments with humans, which may reveal strategies for improving balance or allow controlled comparisons of conventional prosthesis features. A similar morphology may be effective for autonomous devices.

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