Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization

Distributed learning paradigms, such as federated or decentralized learning, allow a collection of agents to solve global learning and optimization problems through limited local interactions. Most such strategies rely on a mixture of local adaptation and aggregation steps, either among peers or at a central fusion center. Classically, aggregation in distributed learning is based on averaging, which is statistically efficient, but susceptible to attacks by even a small number of malicious agents. This observation has motivated a number of recent works, which develop robust aggregation schemes by employing robust variations of the mean. We present a new attack based on sensitivity curve maximization (SCM), and demonstrate that it is able to disrupt existing robust aggregation schemes by injecting small, but effective perturbations

Robust and Efficient Aggregation for Distributed Learning

Distributed learning paradigms, such as federated and decentralized learning, allow for the coordination of models across a collection of agents, and without the need to exchange raw data. Instead, agents compute model updates locally based on their available data, and subsequently share the update model with a parameter server or their peers. This is followed by an aggregation step, which traditionally takes the form of a (weighted) average. Distributed learning schemes based on averaging are known to be susceptible to outliers. A single malicious agent is able to drive an averaging-based distributed learning algorithm to an arbitrarily poor model. This has motivated the development of robust aggregation schemes, which are based on variations of the median and trimmed mean. While such procedures ensure robustness to outliers and malicious behavior, they come at the cost of significantly reduced sample efficiency. This means that current robust aggregation schemes require significantly higher agent participation rates to achieve a given level of performance than their mean-based counterparts in non-contaminated settings. In this work we remedy this drawback by developing statistically efficient and robust aggregation schemes for distributed learning

Networked Signal and Information Processing

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources

Dif-MAML: Decentralized multi-agent meta-learning

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited for this decentralized setting, where the learner benefits from information and computational power spread across the agents. Motivated by this observation, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, robustness, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting

Izloženost ambijentalnomu duhanskomu dimu na radnome mjestu u Makedoniji: kako sada stojimo?

To assess the prevalence and the level of exposure to environmental tobacco smoke (ETS) in the workplace after the enactment of the law restricting indoor smoking in Macedonia, we performed a cross-sectional, self-administered questionnaire study including 372 never-smoking workers recruited from six workplaces. We found a high prevalence of workers exposed to ETS in the workplace (27.4 %) with no significant difference between particular occupation groups. We found no significant difference in the prevalence of passive smokers in the workplace between this study and our study conducted before the law was enacted (31.5 % vs. 27.4 %, P=0.324). The prevalence of workers exposed to ETS for less than three hours a day was significantly lower than of passive smokers with longer exposure (28.4 % vs. 71.6 %, P=0.038). The prevalence of workers exposed to ETS from less than 10 cigarettes smoked by coworkers per day was lower than the prevalence of workers with higher exposure, but statistical significance was not reached (37.9 % vs. 62.1 %, P=0.087). Our findings indicate a high prevalence and a high level of exposure to ETS in the workplace, which calls for stricter adherence to smoking-free legislation or even the total ban of smoking in the workplace.Ovo je ispitivanje obuhvatilo 372 radnika na šest različitih radnih mjesta koji nikad nisu pušili kako bi se procijenila zastupljenost osoba izloženih duhanskomu dimu na radnome mjestu i razina njihove izloženosti nakon zakonskih ograničenja pušenja u zatvorenim prostorijama u Makedoniji. Ispitivanje je provedeno s pomoću upitnika koji su radnici ispunjavali sami. Utvrdili smo visoku zastupljenost radnika izloženih ambijentalnomu duhanskomu dimu na radnome mjestu (27,4 %) te nisu zamijećene statistički značajne razlike među zanimanjima. Nisu uočene značajne razlike između zastupljenosti pasivnih pušača na radnome mjestu u ovome ispitivanju i u našem ranijem ispitivanju, kada još nije na snagu stupio zakon o ograničenju pušenja (31,5 % naprema 27,4 %, P=0,324). Zastupljenost radnika izloženih ambijentalnomu duhanskomu dimu ne dulje od tri sata na dan bila je statistički značajno niža negoli onih čija je izloženost trajala duže (28,4 % naprema 71,6 %, P=0,038). Zastupljenost radnika koji su bili izloženi dimu kolega koji su pušili manje od 10 cigareta na dan bila je niža negoli onih s većom izloženosti, ali razlika nije bila statistički značajna (37,9 % naprema 62,1 %, P=0,087). Naši rezultati potvrđuju da i dalje postoje visoka zastupljenost izloženih radnika i visoke razine izloženosti ambijentalnomu duhanskomu dimu na radnome mjestu, što upućuje na potrebu uvođenja strožih zakona o zabrani pušenja

Essential Medicines at the National Level : The Global Asthma Network's Essential Asthma Medicines Survey 2014

Patients with asthma need uninterrupted supplies of affordable, quality-assured essential medicines. However, access in many low- and middle-income countries (LMICs) is limited. The World Health Organization (WHO) Non-Communicable Disease (NCD) Global Action Plan 2013-2020 sets an 80% target for essential NCD medicines' availability. Poor access is partly due to medicines not being included on the national Essential Medicines Lists (EML) and/or National Reimbursement Lists (NRL) which guide the provision of free/subsidised medicines. We aimed to determine how many countries have essential asthma medicines on their EML and NRL, which essential asthma medicines, and whether surveys might monitor progress. A cross-sectional survey in 2013-2015 of Global Asthma Network principal investigators generated 111/120 (93%) responses41 high-income countries and territories (HICs); 70 LMICs. Patients in HICs with NRL are best served (91% HICs included ICS (inhaled corticosteroids) and salbutamol). Patients in the 24 (34%) LMICs with no NRL and the 14 (30%) LMICs with an NRL, however no ICS are likely to have very poor access to affordable, quality-assured ICS. Many LMICs do not have essential asthma medicines on their EML or NRL. Technical guidance and advocacy for policy change is required. Improving access to these medicines will improve the health system's capacity to address NCDs.Peer reviewe