Personne

Ali H. Sayed

Publications associées (142)

Quantization for Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif, Marco Carpentiero

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Design of a Tap-Amplitude-Based Block Proportional Adaptive Filtering Algorithm

Ali H. Sayed

Proportional-type algorithms have attracted much attention because of their fast convergence ability for sparse system identification. To overcome the drawbacks of existing block proportional methods stemming from inadequate block partitioning, this articl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Compressed Distributed Regression over Adaptive Networks

Ali H. Sayed

We examine the learning performance achievable by a network of agents that solve a distributed regression problem using the recently proposed ACTC (Adapt-Compress-Then-Combine) diffusion strategy. The agents operate under communication constraints: they ar ...

IEEE2023

Discovering Influencers in Opinion Formation Over Social Graphs

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia, Mert Cemri

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Partial Information Sharing Over Social Learning Networks

Ali H. Sayed, Virginia Bordignon

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Policy Evaluation in Decentralized POMDPs With Belief Sharing

Ali H. Sayed, Mert Kayaalp

Most works on multi-agent reinforcement learning focus on scenarios where the state of the environment is fully observable. In this work, we consider a cooperative policy evaluation task in which agents are not assumed to observe the environment state dire ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Social Learning under Randomized Collaborations

Ali H. Sayed

We study a social learning scheme where at every time instant, each agent chooses to receive information from one of its neighbors at random. We show that under this sparser communication scheme, the agents learn the truth eventually and the asymptotic con ...

IEEE2023

The Role of Memory in Social Learning When Sharing Partial Opinions

Ali H. Sayed, Virginia Bordignon

In social learning, a group of agents linked by a graph topology collect data and exchange opinions on some topic of interest, represented by a finite set of hypotheses. Traditional social learning algorithms allow all agents in the network to gain full co ...

IEEE2023

Performance of Social Machine Learning under Limited Data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

This paper studies the non-Asymptotic classification performance of the social machine learning strategy. This strategy involves an independent training phase followed by a cooperative inference phase to classify a growing number of samples. By considering ...

IEEE2023

Privatized graph federated learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

SPRINGER2023