FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

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.

Artificial Intelligence has revolutionized numerous fields, driving advancements in healthcare, finance, and autonomous systems. At the core of this revolution lies Deep Learning---algorithms that learn representations from data through computational layer ...

The simulation of electromagnetic devices with complex geometries and large-scale discrete systems benefits from advanced computational methods like IsoGeometric Analysis and Domain Decomposition. In this paper, we employ both concepts in an Isogeometric T ...

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

Deep reinforcement learning (DRL) is a powerful method for local motion planning in automated driving. However, training of DRL agents is difficult and subject to instability. We propose Regularized Adversarial Fine-Tuning (RAFT), an adversarial DRL traini ...

Automatic speech recognition (ASR) and spoken language understanding (SLU) is the core component of current voice-powered AI assistants such as Siri and Alexa. It involves speech transcription with ASR and its comprehension with natural language understand ...

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume ...

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 ...

Perovskite solar cells are among the most dominant emerging PV technologies which have shown unparallel improvement in power conversion efficiency in just over a decade. Long charge carrier diffusion lengths and lifetimes, high absorption coefficients, ban ...

We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

Many pathologies cause impairments in the speech production mechanism resulting in reduced speech intelligibility and communicative ability. To assist the clinical diagnosis, treatment and management of speech disorders, automatic pathological speech asses ...

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

YY We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performan ...

Domain generalization (DG) tackles the problem of learning a model that generalizes to data drawn from a target domain that was unseen during training. A major trend in this area consists of learning a domain-invariant representation by minimizing the disc ...

Active Debris Removal missions consist of sending a satellite in space and removing one or more debris from their current orbit. A key challenge is to obtain information about the uncooperative target. By gathering the velocity, position, and rotation of t ...

In the framework of the XFLEX HYDRO H2020 European Project, a 5 MW reversible Francis pump-turbine prototype, equipped with an industrial full size frequency converter, is investigated to demonstrate enhanced grid regulation capacities of variable speed hy ...

In recent years, power systems have evolved in physical and cyber-physical layers. In the physical layer, the changes are motivated by environmental concerns resulting in the integration of new types of generation/demand/storage into the grid. These integr ...

In the correlation clustering problem the input is a signed graph where the sign indicates whether each pair of points should be placed in the same cluster or not. The goal of the problem is to compute a clustering which minimizes the number of disagreemen ...

The phenomenon of online dating via web and mobile phone applications involves several actors: graphical interfaces, developers, algorithmic systems for user matching, and users. These actors have been studied in parallel by the social sciences and by comp ...

Could a gradient aggregation rule (GAR) for distributed machine learning be both robust and fast? This paper answers by the affirmative through Multi-Bulyan. Given n workers, f of which are arbitrary malicious (Byzantine) and m = n − f are not, we prove th ...

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Chat with Graph Search

How data structures affect generalization in Kernel Methods and Deep Learning

Tree–cotree-based tearing and interconnecting for 3D magnetostatics: A dual–primal approach

Adaptive energy reference for machine-learning models of the electronic density of states

RAFT: Regularized Adversarial Fine-Tuning to Enhance Deep Reinforcement Learning for Self-Parking

Low-Resource Speech Recognition and Understanding for Challenging Applications

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Multi-agent Learning with Privacy Guarantees

Critical analysis of decision variables for high-throughput experimentation (HTE) with perovskite solar cells

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Automatic pathological speech assessment

Graph Representation Learning with Optimal Transport: Analysis and Applications

Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron

Contrastive Class-aware Adaptation for Domain Generalization

Uncooperative Rendezvous in Space: Design of an Electronic Architecture for High Performance Avionic with Multi Sensor Input and Intensive Data Rate

Discretization Uncertainties of Flow and Fatigue Damage Simulations of a Reversible Francis Pump-Turbine at Off-Design Operation in Turbine Mode

Data-driven Power Electronic Converter Control Design in Power System Applications

Online and Consistent Correlation Clustering

Online Dating Quantification Practices: A Human-Machine Learning Process

Fast and Robust Distributed Learning in High Dimension

Collaborative Sampling in Generative Adversarial Networks