7 research outputs found
A Byzantine-Resilient Aggregation Scheme for Federated Learning via Matrix Autoregression on Client Updates
In this work, we propose FLANDERS, a novel federated learning (FL)
aggregation scheme robust to Byzantine attacks. FLANDERS considers the local
model updates sent by clients at each FL round as a matrix-valued time series.
Then, it identifies malicious clients as outliers of this time series by
comparing actual observations with those estimated by a matrix autoregressive
forecasting model. Experiments conducted on several datasets under different FL
settings demonstrate that FLANDERS matches the robustness of the most powerful
baselines against Byzantine clients. Furthermore, FLANDERS remains highly
effective even under extremely severe attack scenarios, as opposed to existing
defense strategies
Whatâs going on in my city? Recommender systems and electronic participatory budgeting
In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities â Cambridge, Miami and New York Cityâ, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area
Meeting Peopleâs Needs in a Fully Interoperable Domotic Environment
The key idea underlying many Ambient Intelligence (AmI) projects and applications is context awareness, which is based mainly on their capacity to identify users and their locations. The actual computing capacity should remain in the background, in the periphery of our awareness, and should only move to the center if and when necessary. Computing thus becomes âinvisibleâ, as it is embedded in the environment and everyday objects. The research project described herein aims to realize an Ambient Intelligence-based environment able to improve usersâ quality of life by learning their habits and anticipating their needs. This environment is part of an adaptive, context-aware framework designed to make todayâs incompatible heterogeneous domotic systems fully interoperable, not only for connecting sensors and actuators, but for providing comprehensive connections of devices to users. The solution is a middleware architecture based on open and widely recognized standards capable of abstracting the peculiarities of underlying heterogeneous technologies and enabling them to co-exist and interwork, without however eliminating their differences. At the highest level of this infrastructure, the Ambient Intelligence framework, integrated with the domotic sensors, can enable the system to recognize any unusual or dangerous situations and anticipate health problems or special user needs in a technological living environment, such as a house or a public space