Sustainability and Ethicality are Peripheral to Students’ Software Design

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

We present TimberTool (TTool v2.1.1), a software designed for woodworking tasks assisted by augmented reality (AR), emphasizing its essential function of the real-time localization of a tool head’s poses within camera frames. The localization process, a fu ...

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, priv ...

Deepfakes - synthetic videos generated by machine learning models - are becoming increasingly sophisticated. While they have several positive use cases, their potential for harm is also high. Deepfake production involves input from multiple engineers, maki ...

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...

Exploring a wide range of design options is crucial for every sound conceptual design process. In the conventional structural design workflow, optimization techniques are generally employed to generate well-performing design options. However, focusing only ...

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts or to reveal evolving publication practices of historical media. This research is often ...

Defining a universal metric for Quality of Experience (QoE) is notoriously hard due to the complex relationship between low-level performance metrics and user satisfaction. The most common metric, the Mean Opinion Score (MOS), has well-known biases and inc ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

Driven by the rising popularity of chatbots such as ChatGPT, there is a budding line of research proposing guidelines for chatbot design, both in general and specifically for digital education. Nevertheless, few researchers have focused on providing concep ...

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

Sustainability and Ethicality are Peripheral to Students’ Software Design

Chat with Graph Search

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Differentially private multi-agent constraint optimization

The Privacy Power of Correlated Noise in Decentralized Learning

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Secure Interface Design Leveraging Hardware/Software Support

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

A multimodal measurement of the impact of deepfakes on the ethical reasoning and affective reactions of students

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Computational conceptual design — typological exploration of spatial truss systems through optimization

Hardware-Software co-design Methodologies for Edge AI Optimization

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

impresso Text Reuse at Scale. An interface for the exploration of text reuse data in semantically enriched historical newspapers

Learning a QoE Metric from Social Media and Gaming Footage

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

TRACE: A Conceptual Model to Guide the Design of Educational Chatbots

Privacy-Preserving Machine Learning on Graphs

Multi-agent Learning with Privacy Guarantees