Publications related to Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Christophe Ballif, Marine Dominique Cauz, Laure-Emmanuelle Perret Aebi

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

2023

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Erixhen Sula

Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular network ...

EPFL2021

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and—in asynchronous implementations—on the gradient staleness. Especially, it has been observed that the spe ...

2021

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Ali H. Sayed, Sulaiman A S A E Alghunaim, Ming Yan

This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

IEEE2020

SCAFFOLD Stochastic Controlled Averaging for Federated Learning

Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy, Ananda Theertha Suresh

Federated Averaging (FEDAVG) has emerged as the algorithm of choice for federated learning due to its simplicity and low communication cost. However, in spite of recent research efforts, its performance is not fully understood. We obtain tight convergence ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Wolfram Wiesemann

A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these nominal distributions are themselves estimated from data, which makes them s ...

2019

Scalable Convex Optimization Methods for Semidefinite Programming

Alp Yurtsever

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, contemporary applications in science and engineering impose heavy computational and storage burdens on the optimization algorithms. As a result, there ...

EPFL2019

Learning with Structured Sparsity: From Discrete to Convex and Back.

Marwa El Halabi

In modern-data analysis applications, the abundance of data makes extracting meaningful information from it challenging, in terms of computation, storage, and interpretability. In this setting, exploiting sparsity in data has been essential to the developm ...

EPFL2018

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

A first-order primal-dual method with adaptivity to local smoothness

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

SCAFFOLD Stochastic Controlled Averaging for Federated Learning

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

Scalable Convex Optimization Methods for Semidefinite Programming

Learning with Structured Sparsity: From Discrete to Convex and Back.

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Scalable Convex Optimization Methods for Semidefinite Programming

Learning with Structured Sparsity: From Discrete to Convex and Back.

A first-order primal-dual method with adaptivity to local smoothness

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

SCAFFOLD Stochastic Controlled Averaging for Federated Learning