Recent efforts in medical image computing have focused on improving fairness by balancing it with accuracy within a single, unified model. However, this often creates a trade-off: gains for underrepresented groups can come at the expense of reduced accuracy for groups that were previously well-served. In high-stakes clinical contexts, even minor drops in accuracy can lead to serious consequences, making such trade-offs highly contentious. Rather than accepting this compromise, we reframe the fairness objective in this paper as maximizing diagnostic accuracy for each patient group by leveraging additional computational resources to train group-specific models. To achieve this goal, we introduce SPARE, a novel data reweighting algorithm designed to optimize performance for a given group. SPARE evaluates the value of each training sample using two key factors: utility, which reflects the sample's contribution to refining the model's decision boundary, and group similarity, which captures its relevance to the target group. By assigning greater weight to samples that score highly on both metrics, SPARE rebalances the training process-particularly leveraging the value of out-of-group data-to improve group-specific accuracy while avoiding the traditional fairness-accuracy trade-off. Experiments on two skin disease datasets demonstrate that SPARE significantly improves group-specific performance while maintaining comparable fairness metrics, highlighting its promise as a more practical fairness paradigm for improving clinical reliability.

With the emerging application of Federated Learning (FL) in finance, hiring and healthcare, FL models are regulated to be fair, preventing disparities with respect to legally protected attributes such as race or gender. Two concepts of fairness are important in FL: global and local fairness. Global fairness addresses the disparity across the entire population and local fairness is concerned with the disparity within each client. Prior fair FL frameworks have improved either global or local fairness without considering both. Furthermore, while the majority of studies on fair FL focuses on binary settings, many real-world applications are multi-class problems. This paper proposes a framework that investigates the minimum accuracy lost for enforcing a specified level of global and local fairness in multi-class FL settings. Our framework leads to a simple post-processing algorithm that derives fair outcome predictors from the Bayesian optimal score functions. Experimental results show that our algorithm outperforms the current state of the art (SOTA) with regard to the accuracy-fairness tradoffs, computational and communication costs. Codes are available at: \url{https://github.com/papersubmission678/The-cost-of-local-and-global-fairness-in-FL.}

Recent emerging technologies such as artificial intelligence and machine learning have been promising enormous economic and societal benefits. While it is desirable to deploy these technologies to Internet-of-Things (IoT) infrastructures in many applications such as medical, energy, transportation, and industrial automation systems, such deployments present daunting challenges in performance, efficiency, and dependability of scaling-up IoT infrastructure, due to the ever-increasing number of edge devices, ever-increasing levels of device and system heterogeneity, and more stringent requirements of reliability, robustness, and security in mission-critical settings. This position paper elaborates the needs for a cross-layer and full hardware/software stack solution for the design and deployment of scalable, secure, and smart mission-critical IoT systems from four different perspectives and research fields. We present a review of recent studies on such issues and identify the potential challenges and gaps, based on which we highlight some important research directions and future works that can be conducted to tackle such challenges.

Along with the rapid development and deployment of real-time wireless network (RTWN) technologies in a wide range of applications, effective packet scheduling algorithms have been playing a critical role in RTWNs for achieving desired Quality of Service (QoS) for real-time sensing and control, especially in the presence of unexpected disturbances. Most existing solutions in the literature focus either on static or dynamic schedule construction to meet the desired QoS requirements, but have a common assumption that all wireless links are reliable. Although this assumption simplifies the algorithm design and analysis, it is not realistic in real-life settings. To address this drawback, this paper introduces a novel reliable dynamic packet scheduling framework, called RD-PaS. RD-PaS can not only construct static schedules to meet both the timing and reliability requirements of end-to-end packet transmissions in RTWNs for a given periodic network traffic pattern, but also construct new schedules rapidly to handle abruptly increased network traffic induced by unexpected disturbances while minimizing the impact on existing network flows. The functional correctness of the RD-PaS framework has been validated through its implementation and deployment on a real-life RTWN testbed. Extensive simulation-based experiments have also been performed to evaluate the effectiveness of RD-PaS, especially in large-scale network settings.

Today's embedded systems are exposed to variations in resource usage due to complex software applications, hardware platforms, and impact of the runtime environments. When these variations are large and efficiency is required, on-line resource managers may be deployed on the system to help it control its resource usage. An often neglected problem is whether these resource managers are stable, meaning that the resource usage is controlled under all possible scenarios. In distributed systems, this problem is particularly hard because applications distributed over many resources generate complex dependencies between their resources. In this article, we develop a mathematical model of the system, and derive conditions that, if satisfied, guarantee stability.

Event triggered systems are feedback systems that sample the state when the novelty in that state exceeds a threshold. Prior work has demonstrated that event-triggered feedback may have inter-sampling intervals that are, on average, greater than the sampling periods found in comparably performing periodic sampled data systems. This fact has been used to justify the claim that event-triggered systems are more efficient in their use of communication or computational resources than periodic sampled data systems. If, however, one accounts for quantization effects and maximum acceptable delays, then it is quite possible that the actual bit-rates generated by event triggered systems may be greater than that of periodically triggered systems. This paper examines the bit-rates required to asymptotically stabilize nonlinear event triggered systems. An increasing upper bound on the stabilizing bit-rate with respect to the norm of the state is derived. This increasing upper bound on the stabilizing bit-rate reveals the efficient attentiveness property of event triggered systems, i.e. the farther the state is away from the origin, the higher the stabilizing bit-rate will be. Moreover, this paper presents the conditions under which the stabilizing bit-rates asymptotically go to 0.

A wireless networked control systems (NCS) is a control system whose feedback path is realized over a wireless communication network. The stability of such systems can be problematic given the random way in which wireless channels drop feedback messages. This paper establishes sufficient conditions for the almost sure stability of NCS under random dropouts. These conditions relate the burstiness in the dropout process to the nominal response of the controlled system. In particular, this means that the burstiness of the dropout process provides a convenient quality-of-service (QoS) constraint on the wireless channel that can be used to adaptively reconfigure the control system in a manner that guarantees the almost sure stability of the NCS. We also show how a probabilistic extension of the network calculus can be used to reconfigure multi-hop communication networks so this paper's sufficient stability condition is not violated.

For many control systems, control performance is strongly dependent on delay variations of the control tasks. Such variations can come from a number of sources including task preemptions, variations in task workloads and perturbations in the physical environment. Existing work has considered improving control task delay variations due to task preemption only. This paper presents a general adaptive framework that incorporates a powerful heuristic aiming to further reduce delay variations. Preliminary results indicate that the heuristic significantly improves existing approaches.

introduction Share on Introduction to the special issue on self-adaptive and self-organizing wireless networking systems Authors: Michael Lemmon Department of Electrical Engineering, University of Notre Dame, USA Department of Electrical Engineering, University of Notre Dame, USAView Profile , Christian Poellabauer Department of Computer Science and Engineering, University of Notre Dame, USA Department of Computer Science and Engineering, University of Notre Dame, USAView Profile , Liqiang Zhang Department of Computer and Information Sciences, Indiana University South Bend, USA Department of Computer and Information Sciences, Indiana University South Bend, USAView Profile , Xiaobo Zhou Department of Computer Science, University of Colorado at Colorado Springs, USA Department of Computer Science, University of Colorado at Colorado Springs, USAView Profile Authors Info & Claims ACM Transactions on Autonomous and Adaptive SystemsVolume 4Issue 3Article No.: 15pp 1–4https://doi.org/10.1145/1552297.1552298Published:24 July 2009Publication History 1citation361DownloadsMetricsTotal Citations1Total Downloads361Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The elastic task model is a powerful model for adapting periodic real-time systems in the presence of uncertainty. This work generalizes the existing elastic scheduling approach in several directions. First, it presents a general framework, which formulates a trade-off between task schedulability and a specific performance metric as an optimization problem. Such a framework allows real-time systems under overloads to graciously adapt by adjusting their performance level. Second, it is shown in this work that the well-known task compression algorithm in fact solves a quadratic programming problem that seeks to minimize the sum of the squared deviation of a task's utilization from initial desired utilization. This finding indicates that the task compression algorithm may be applied to efficiently solve other similar types of problems that often arise in real-time applications. In particular, an iterative approach is proposed to solve the period selection problem for real-time tasks with deadlines less than respective periods. Further, the framework is adapted to solve the deadline selection problem, which is useful in some control systems with fixed periods.

Task period adaptations are often used to alleviate temporal overload conditions in real-time systems. Existing frameworks assume that only task periods are adjustable and that task deadlines remain unchanged at all times. This paper formally introduces a more general real-time task model where task deadlines, which are less than or equal to task periods, are functions of task periods. This tight coupling between task deadlines and task periods has been discussed in a recent work in control systems and presents a novel real-time scheduling challenge. To solve the period and deadline selection problem, this article identifies a feasible period-deadline combination and proposes a heuristic, which iteratively adjusts task periods and deadlines in such a way that the task set becomes schedulable. Experimental results show that the heuristic finds a solution to the period and deadline selection problem over 73% of the time, using less than three search iterations. When it is unable to find a solution to the problem, the heuristic requires less than 0.02s to run in the worst-case (with at most 100 search iterations).

The elastic task model (Buttazzo et al., 2002) is a powerful model for adapting real-time systems in the presence of uncertainty. This paper generalizes the existing elastic scheduling approach in several directions. It reveals that the original task compression algorithm in (Buttazzo et al., 2002) in fact solves a quadratic programming problem that seeks to minimize the sum of the squared deviation of a task's utilization from initial desired utilization. This finding indicates that the task compression algorithm may be applied to efficiently solve other similar types of problems. In particular, an iterative approach is proposed to solve the task compression problem for real-time tasks with deadlines less than respective periods. Furthermore, a new objective for minimizing the average difference of task periods from desired values is introduced and a closed-form formula is derived for solving the problem without recursion

Many real-time systems have firm real-time requirements which allow occasional deadline violations but discard any jobs that are not finished by their deadlines. To measure the performance of such a system, a quality of service (QoS) metric is needed. Examples of often used QoS metrics for firm real-time systems are average deadline miss rates and (m, k)-firm constraints. However, for certain applications, these metrics may not be adequate measures of system performance. This paper introduces a novel QoS constraint for firm real-time systems. The new QoS constraint generalizes existing firm real-time constraints. Furthermore, using networked control system as an example, we show that this constraint can be directly related to the control system's performance. We then present three different scheduling approaches with respect to this QoS constraint. Experimental results are provided to show the effectiveness of these approaches.

Markov-chain (MC) based constraints have been shown to be an effective QoS measure for a class of real-time systems, particularly those arising from control applications. Scheduling tasks with MC constraints introduces new challenges because these constraints require not only specific task finishing patterns but also certain task completion probability. Multiple tasks with different MC constraints competing for the same resource further complicates the problem. In this paper, we study the problem of scheduling multiple tasks with different MC constraints. We present two scheduling approaches which (i) lead to improvements in "overall" system performance, and (ii) allow the system to achieve graceful degradation as system load increases. The two scheduling approaches differ in their complexities and performances. We have implemented our scheduling algorithms in the QNX real-time operating system environment and used the setup for several realistic control tasks. Data collected from the experiments as well as simulation all show that our new scheduling algorithms outperform algorithms designed for window-based constraints as well as previous algorithms designed for handling MC constraints.

Many real-time systems have firm real-time requirements which allow occasional deadline violations but discard tasks that are not finished by their deadlines. To measure the goodness of such a system, a quality of service (QoS) metric is needed. Examples of often used QoS metrics for firm real-time systems are average deadline miss rates and (m,k)-firm constraint. However, for certain applications, these metrics may not be adequate measures of system performance. This paper introduces a novel QoS constraint for networked feedback control systems. We show that this constraint can be directly related to the control system's performance. We then present three different scheduling approaches with respect to this QoS constraint. Experimental results are provided to compare these approaches.

Overload management policies avoid network congestion by actively dropping packets. This paper studies the effect that such data dropouts have on the performance of spatially distributed control systems. We formally relate the spatially-distributed system's performance (as measured by the average output signal power) to the data dropout rate. This relationship is used to pose an optimization problem whose solution is a Markov chain characterizing a dropout process that maximizes control system performance subject to a specified lower bound on the dropout rate. We then use this Markov chain to formulate an overload management policy that enables nodes to enforce the "optimal" dropout process identified in our optimization problem. Simulation experiments are used to verify the paper's claims.

This paper examines a clock synchronization algorithm in which processors identify dynamic models of neighboring processor clocks. These models are then used as signature functions to develop a clock synchronization algorithm that functions in the presence of drifting clocks.

This paper demonstrates the use of supervisory control theory in synthesizing plug-ins for distributed software. The plug-ins are software objects that supervise an existing distributed system so that certain properties such as fairness and deadlock freedom are guaranteed. The distributed application is modeled as a bounded ordinary Petri net and system analysis is accomplished through a partial order method known as unfolding. The unfolding constructs an event structure that provides a natural encapsulation of concurrent threads of execution whose selective disablement by the supervisory plug-in assures the desired application property. The synthesis of the plug-in is based on results from supervisory control theory and the synthesized plug-ins are optimal in that they are maximally permissive. We demonstrate our approach on a distributed cache system.

In this paper, the supervisory control of hybrid systems is introduced and discussed at length. Such control systems typically arise in the computer control of continuous processes, for example, in manufacturing and chemical processes, in transportation systems, and in communication networks. A functional architecture of hybrid control systems consisting of a continuous plant, a discrete-event controller, and an interface is used to introduce and describe analysis and synthesis concepts and approaches. Our approach highlights the interaction between the continuous and discrete dynamics, which is the cornerstone of any hybrid system study. Discrete abstractions are used to approximate the continuous plant. Properties of the discrete abstractions to be appropriate representations of the continuous plant are presented, and important concepts such as determinism and controllability are discussed. Supervisory control design methodologies are presented to satisfy control specifications described by formal languages. Several examples are used throughout the paper to illustrate our approach.

Modular neural networks use a single gating neuron to select the outputs of a collection of agent neurons. Expectation-maximization (EM) algorithms provide one way of training modular neural networks to approximate non-linear functionals. This paper introduces a hybrid interior-point (HIP) algorithm for training modular networks. The HIP algorithm combines an interior-point linear programming (LP) algorithm with a Newton-Raphson iteration in such a way that the computational efficiency of the interior point LP methods is preserved. The algorithm is formally proven to converge asymptotically to locally optimal networks with a total computational cost that scales in a polynomial manner with problem size. Simulation experiments show that the HIP algorithm produces networks whose average approximation error is better than that of EM-trained networks. These results also demonstrate that the computational cost of the HIP algorithm scales at a slower rate than the EM-procedure and that, for small-size networks, the total computational costs of both methods are comparable.

Hybrid control systems consist of a discrete event (DES) controller supervising a continuous-state (CSS) plant. A controller can be synthesized by obtaining a DES controller for an equivalent DES representation (DES plant) of the CSS plant. An important issue concerns the logical invariance (stability) of DES plant transitions to variations in the initial CSS plant state. This paper provides a set of sufficient conditions for the existence of stable transitions in the DES plant. For CSS plants which are affine in their control policies, these conditions form a system of linear inequalities over the space of control vectors used by the CSS plant. Feasible points to this inequality system are inductively determined using a method of centers algorithm known as the ellipsoid method.

This paper presents an alternating minimization (AM) algorithm used in the training of radial basis function and linear regressor networks. The algorithm is a modification of a small-step interior point method used in solving primal linear programs. The algo(cid:173) rithm has a convergence rate of O( fo,L) iterations where n is a measure of the network size and L is a measure of the resulting solution's accuracy. Two results are presented that specify how aggressively the two steps of the AM may be pursued to ensure convergence of each step of the alternating minimization.

Competitive learning paradigms are usually defined with winner-take-all (WTA) activation rules. The paper develops a mathematical model for competitive learning paradigms using a generalization of the WTA activation rule (g-WTA). The model is a partial differential equation (PDE) relating the time rate of change in the ;density' of weight vectors to the divergence of a vector field called the neural flux. Characteristic trajectories are used to study solutions of the PDE model over scalar weight spaces. These solutions show how the model can be used to design competitive learning algorithms which estimate the modes of unknown probability density functions.

This paper proposes a neural network solution to path planning by two degree-of-freedom (DOF) robots. The proposed network is a two-dimensional sheet of neurons forming a distributed representation of the robot's workspace. Lateral interconnections between neurons are "cooperative," so that the field exhibits oscillatory behavior. This paper shows how that oscillatory behavior can be used to solve the path-planning problem. The results reported show that the proposed neural network finds the variational solution of Bellman's dynamic programming equation.

The competitively inhibited neural network (CINN) is a competitive learning paradigm which is modeled by a collection of ordinary differential equations. A sliding threshold condition has been derived for determining the activity of a CINN neuron. This condition allows the development of a mathematical model for CINN learning. The model is a nonlinear diffusion equation whose solution quantitatively characterizes the learning process. Simulation experiments have validated this model

The competitively inhibited neural net (CINN) is a special class of competitive learning paradigm which is amenable to formal analysis. The authors present evidence that the CINN is capable of globally optimizing certain problems. They suggest that the CINN is capable of locating the primary mode of a source density function. In the event that this density represents a performance functional (such as in maximum-likelihood estimation), the CINN can be used to locate the global optimum of the performance functional. The mechanisms behind this global search property have been explained using a nonlinear diffusion model of CINN learning, and simulation experiments have corroborated this capability of the CINN for a minimally deceptive problem