Machine learning models trained on retinal images have shown great potential in diagnosing various diseases. However, effectively training these models, especially in resource-limited regions, is often impeded by a lack of diverse data. Federated learning (FL) offers a solution to this problem by utilizing distributed data across a network of clients to enhance the training dataset volume and diversity. Nonetheless, significant privacy concerns have been raised for this approach, notably due to gradient inversion attacks that could expose private patient data used during FL training. Therefore, it is crucial to assess the vulnerability of FL models to such attacks because privacy breaches may discourage data sharing, potentially impacting the models' generalizability and clinical relevance. To tackle this issue, we introduce a novel framework to evaluate the vulnerability of federated deep learning models trained using retinal images to gradient inversion attacks. Importantly, we demonstrate how publicly available data can be used to enhance the quality of reconstructed images through an innovative image-to-image translation technique. The effectiveness of the proposed method was measured by evaluating the similarity between real fundus images and the corresponding reconstructed images using three different convolutional neural network architectures: ResNet-18, VGG-16, and DenseNet-121. Experimental results for the task of retinal age prediction demonstrate that, across all models, over 92 % of the participants in the training set could be identified from their reconstructed retinal vessel structure alone. Furthermore, even with the implementation of differential privacy countermeasures, we show that substantial information can still be extracted from the reconstructed images. Therefore, this work underscores the urgent need for improved defensive strategies to safeguard patient privacy during federated learning.

OBJECTIVES: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. MATERIALS AND METHODS: The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. RESULTS: Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001). DISCUSSION: The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. CONCLUSION: The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.

Legible motion is intent-expressive, which when employed during social robot navigation, allows others to quickly infer the intended avoidance strategy. Predictable motion matches an observer's expectation which, during navigation, allows others to confidently carryout the interaction. In this work, we present a navigation framework capable of reasoning on its legibility and predictability with respect to dynamic interactions, e.g., a passing side. Our approach generalizes the previously formalized notions of legibility and predictability by allowing dynamic goal regions in order to navigate in dynamic environments. This generalization also allows us to quantitatively evaluate the legibility and the predictability of trajectories with respect to navigation interactions. Our approach is shown to promote legible behavior in ambiguous scenarios and predictable behavior in unambiguous scenarios. In a multi-agent environment, this yields an increase in safety while remaining competitive in terms of goal-efficiency when compared to other robot navigation planners in multi-agent environments. The code of this work is made publicly available1.

This paper presents a path following control designed for human-robot collaborative tasks. The path following strategy allows the task description and human-robot interaction to be defined according to components tangent and transversal to a nominal path. With this formulation, the control objectives for each task can be customized to generate motions towards task completion and getting on the nominal path when switching between tasks. Simulation examples of the path following control is presented, demonstrating the ability for the proposed controllers to be customized according to objectives of the human-robot collaboration.

In this paper the fusion of data from a network of security cameras and RSS fingerprint observations are combined to facilitate the simultaneous tracking of multiple persons inside indoor environments. An objective of the developed algorithm is to utilize existing building infrastructure namely the networks of security cameras and WiFi access points. Additionally minimal initial and maintenance calibration is required as crowdsourcing of the fingerprint mapping and self-calibrating camera processing is an integral component of the algorithm. Experimental results are given that demonstrate the accuracy, robustness and adaptability of the developed tracking algorithm.

This paper presents an approach for designing path-following controllers for the kinematic model of car-like mobile robots using transverse feedback linearization with dynamic extension. This approach is applicable to a large class of paths and its effectiveness is experimentally demonstrated on a Chameleon R100 Ackermann steering robot. Transverse feedback linearization makes the desired path attractive and invariant, while the dynamic extension allows the closed-loop system to achieve the desired motion along the path.

Path following controllers make the output of a control system approach and traverse a prespecified path with noa prioritime-parametrization. In this paper, we present a method for path following control design applicable to framed curves generated by splines in the workspace of kinematically redundant mechanical systems. The class of admissible paths includes self-intersecting curves. Kinematic redundancies are resolved by designing controllers that solve a suitably defined constrained quadratic optimization problem. By employing partial feedback linearization, the proposed path following controllers have a clear physical meaning. The approach is experimentally verified on a four-degree-of-freedom (four-DOF) manipulator with a combination of revolute and linear actuated links and significant model uncertainty.

Path following controllers make the output of a control system approach and traverse a pre-specified path with no a priori time-parametrization. This paper implements a path following controller, based on transverse feedback linearization (TFL), which guarantees invariance of the path to be followed. The coordinate and feedback transformation employed allows one to easily design control laws to generate arbitrary desired motions on the path for the closed-loop system. The approach is applied to an uncertain and simplified model of a robot manipulator for which none of the dynamic parameters are measured. The controller is made robust to modelling uncertainties using Lyapunov redesign. The robustified controller is tested on a 4-degree-of-freedom (4-DOF) manipulator with a combination of revolute and linear actuated links. The experimental results show a substantial improvement when using the robust controller for path following versus standard state feedback.

Virtual holonomic output constraints define a set in the output space of a robot to which the end-effector should be confined. We propose the use of set stabilization for implementing virtual constraints, conducting analyses and experimental comparisons between existing control schemes and a set stabilization controller to justify our choice. The existing methods combine geometric projections and PD control, which ignores the higher-order properties of the constraint like curvature. The set stabilization method inherently incorporates constraint curvature information, allowing it to decouple the dynamics towards and along the set. It is shown that the two methods are equivalent on a line constraint, but that only the set stabilization method simultaneously guarantees asymptotic stability on a circle.