Fast Discrete Consensus Based on Gossip for Makespan Minimization in Networked Systems

In this paper we propose a novel algorithm to solve the discrete consensus problem, i.e., the problem of distributing evenly a set of tokens of arbitrary weight among the nodes of a networked system. Tokens are tasks to be executed by the nodes and the proposed distributed algorithm minimizes monotonically the makespan of the assigned tasks. The algorithm is based on gossip-like asynchronous local interactions between the nodes. The convergence time of the proposed algorithm is superior with respect to the state of the art of discrete and quantized consensus by at least a factor O(n) in both theoretical and empirical comparisons

Imagining the Future in the Neoliberal Era: Young People's Optimism and Their Faith in Hard Work

In the aftermath of the 2008 global economic crisis, the future of young people is often presented in a negative light. Despite the recent difficult circumstances, our mixed-method study found that young people in Britain were still optimistic about their personal future. In this article, we explore the tension between this optimism and the (often less positive) actual circumstances of young people. Our findings suggest that young people’s views of the future were shaped by their deep-seated faith in the transformative power of hard work. We shall argue that this faith results from young people’s psychological adjustments to neoliberal beliefs about the potential of human agency to forge the future, with implications for views of others and society more generally

The use of film documentary in social science research: audio-visual accounts of the ‘migration crisis’ from the Italian island of Lampedusa

The importance of visual evidence – and particularly films and videos – has become more prominent with the fast pace of technological development that has made filming more easily accessible. Since the early 20th century, films have been used as a data collection method in social science research, but less attention has been given to their potential for research dissemination. It is well documented that visual representations are powerful means to broadcast public discourses. The Arab Spring in 2011 and the increasing movement of people across the Mediterranean Sea are a case in point. Images and videos of people trying to reach Europe have contributed to the construction of what is often referred to as the ‘Mediterranean migration crisis’. In this article, we explore the process of making a film documentary about the people in the Italian island of Lampedusa, a key transitory site for migrants, and how they deal with the challenges of this ‘crisis’ while trying to respond to the local struggles of their isolated community. Drawing on the analysis of ‘audio-visual accounts’ – as the filmed verbal elaborations that broadcast themes emerging from social science research – we reflect on the potential and drawbacks of film documentaries for both knowledge production and research dissemination

Novel Stability Conditions for Nonlinear Monotone Systems and Consensus in Multi-Agent Networks

We introduce a novel definition of monotonicity, termed “type-K” in honor of Kamke, and study nonlinear type-K monotone dynamical systems possessing the plus-subhomogeneity property, which we call “K-subtopical” systems after Gunawardena and Keane. We show that type-K monotonicity, which is weaker than strong monotonicity, is also equivalent to monotonicity for smooth systems evolving in continuous-time, but not in discrete-time. K-subtopical systems are proved to converge toward equilibrium points, if any exists, generalizing the result of Angeli and Sontag about convergence of topical systems' trajectories toward the unique equilibrium point when strong monotonicity is considered. The theory provides an new methodology to study the consensus problem in nonlinear multi-agent systems (MASs). Necessary and sufficient conditions on the local interaction rule of the agents ensuring the K-subtopicality of MASs are provided, and consensus is proven to be achieved asymptotically by the agents under given connectivity assumptions on directed graphs. Examples in continuous-time and discrete-time corroborate the relevance of our results in different applications

Dynamic Resilient Containment Control in Multirobot Systems

In this article, we study the dynamic resilient containment control problem for continuous-time multirobot systems (MRSs), i.e., the problem of designing a local interaction protocol that drives a set of robots, namely the followers, toward a region delimited by the positions of another set of robots, namely the leaders, under the presence of adversarial robots in the network. In our setting, all robots are anonymous, i.e., they do not recognize the identity or class of other robots. We consider as adversarial all those robots that intentionally or accidentally try to disrupt the objective of the MRS, e.g., robots that are being hijacked by a cyber–physical attack or have experienced a fault. Under specific topological conditions defined by the notion of (r,s)-robustness, our control strategy is proven to be successful in driving the followers toward the target region, namely a hypercube, in finite time. It is also proven that the followers cannot escape the moving containment area despite the persistent influence of anonymous adversarial robots. Numerical results with a team of 44 robots are provided to corroborate the theoretical findings

Dynamic max-consensus with local self-tuning

This work describes a novel control protocol for multi-agent systems to solve the dynamic max-consensus problem. In this problem, each agent has access to an external timevarying scalar signal and has the objective to estimate and track the maximum among all these signals by exploiting only local communications. The main strength of the proposed protocol is that it is able to self-tune its internal parameters in order to achieve an arbitrary small steady-state error without significantly affecting the convergence time. We employ the proposed protocol in the context of distributed graph parameter estimations, such as size, diameter, and radius, and provide simulations in the scenario of open multi-agent systems. Copyright (C) 2022 The Authors

Selective Trimmed Average: A Resilient Federated Learning Algorithm With Deterministic Guarantees on the Optimality Approximation

The federated learning (FL) paradigm aims to distribute the computational burden of the training process among several computation units, usually called agents or workers, while preserving private local training datasets. This is generally achieved by resorting to a server–worker architecture where agents iteratively update local models and communicate local parameters to a server that aggregates and returns them to the agents. However, the presence of adversarial agents, which may intentionally exchange malicious parameters or may have corrupted local datasets, can jeopardize the FL process. Therefore, we propose selective trimmed average (SETA), which is a resilient algorithm to cope with the undesirable effects of a number of misbehaving agents in the global model. SETA is based on properly filtering and combining the exchanged parameters. We mathematically prove that the proposed algorithm is resilient against data and local model poisoning attacks. Most resilient methods presented so far in the literature assume that a trusted server is in hand. In contrast, our algorithm works both in server–worker and shared memory architectures, where the latter excludes the necessity of a trusted server. The theoretical findings are corroborated through numerical results on MNIST dataset and on multiclass weather dataset (MWD)

Robust reconstruction of the discrete state for a class of nonlinear uncertain switched systems

n/

Accelerated Alternating Direction Method of Multipliers Gradient Tracking for Distributed Optimization

This paper presents a novel accelerated distributed algorithm for unconstrained consensus optimization over static undirected networks. The proposed algorithm combines the benefits of acceleration from momentum, the robustness of the alternating direction method of multipliers, and the computational efficiency of gradient tracking to surpass existing state-of-the-art methods in convergence speed, while preserving their computational and communication cost. First, we prove that, by applying momentum on the average dynamic consensus protocol over the estimates and gradient, we can study the algorithm as an interconnection of two singularly perturbed systems: the outer system connects the consensus variables and the optimization variables, and the inner system connects the estimates of the optimum and the auxiliary optimization variables. Next, we prove that, by adding momentum to the auxiliary dynamics, our algorithm always achieves faster convergence than the achievable linear convergence rate for the non-accelerated alternating direction method of multipliers gradient tracking algorithm case. Through simulations, we numerically show that our accelerated algorithm surpasses the existing accelerated and non-accelerated distributed consensus first-order optimization protocols in convergence speed