Personne

Ali H. Sayed

Publications associées (142)

Detection of Malicious Agents in Social Learning

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

Non-Bayesian social learning is a framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive observations conditioned on the same true state, although it is also possib ...

IEEE Institute of Electrical and Electronics Engineers2024

Social Opinion Formation and Decision Making Under Communication Trends

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Diffusion Optimistic Learning for Min-Max Optimization

Ali H. Sayed

This work introduces and studies the convergence of a stochastic diffusion-optimistic learning (DOL) strategy for solving distributed nonconvex (NC) and Polyak-Lojasiewicz (PL) min-max optimization problems. Problems of this type are of interest due to a w ...

Institute of Electrical and Electronics Engineers Inc.2024

Matching centralized learning performance via compressed decentralized learning with error feedback

Ali H. Sayed

The DEF-ATC (Differential Error Feedback - Adapt Then Combine) approach is a novel strategy to address decentralized learning and optimization problems under communication constraints. The strategy blends differential quantization and error feedback to mit ...

Institute of Electrical and Electronics Engineers Inc.2024

Differential Error Feedback for Communication-Efficient Decentralized Optimization

Ali H. Sayed

Communication-constrained algorithms for decentralized learning and optimization rely on the exchange of quantized signals coupled with local updates. In this context, differential quantization is an effective technique to mitigate the negative impact of q ...

IEEE Computer Society2024

Causal Impact Analysis for Asynchronous Decision Making

Yunus Inan, Ali H. Sayed, Mert Kayaalp

We consider a collaborative decision-making framework where heterogeneous agents receive streaming and partially informative observations. We consider two asynchronous scenarios that differ based on the agents' participation patterns and the fusion center' ...

IEEE2024

Compressed Regression Over Adaptive Networks

Ali H. Sayed

In this work we derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. Agents employ the recently proposed ACTC (adapt-compress-then-combine) diffu ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

DECENTRALIZED FUSION OF EXPERTS OVER NETWORKS

Ali H. Sayed

This article considers a network of agents interested in solving a classification t ask. The datasets available to accomplish the task are heterogeneous and dispersed across the agents. Each agent is inter-ested in discriminating among the 'inner' hypothes ...

IEEE Computer Society2024

Distributed Decision-Making for Community Structured Networks

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

Traditional social learning frameworks consider environments with a homogeneous state where each agent receives observations conditioned on the same hypothesis. In this work, we study the distributed hypothesis testing problem for graphs with a community s ...

Institute of Electrical and Electronics Engineers Inc.2024

Memory-Aware Social Learning Under Partial Information Sharing

Ali H. Sayed, Virginia Bordignon

This work examines a social learning problem, where dispersed agents connected through a network topology interact locally to form their opinions (beliefs) as regards certain hypotheses of interest. These opinions evolve over time, since the agents collect ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Quantization for Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Roula Nassif, Stefan Vlaski, 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