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EURECOM

Academic institutioneurope · fr
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Research library161linked papers
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Selected work

Representative Papers

Universal and Asymptotically Optimal Data and Task Allocation in Distributed Computing

Jan 09, 2026arXiv.org

This work addresses the joint optimization of communication and computation overheads in distributed computing, where a master node coordinates \(N\) workers to compute a set of subfunctions dependent on \(d\) input files. The problem is modeled as a \(d\)-uniform hypergraph edge partitioning task, and a deterministic Interweaved-Cliques (IC) assignment scheme is proposed. This scheme achieves order-optimal communication load (number of files received per worker) and computation load (number of subfunctions processed per worker) without prior knowledge of the subfunction structure. Leveraging an information-theoretically inspired interwoven clique construction and a deterministic allocation strategy, the method applies to any multi-function decomposition satisfying mild density conditions, requires no file reassignment, and attains order-optimal communication cost \(\Theta(n/N^{1/d})\) and computation cost across a broad range of parameters, yielding a partitioning gain of \(N^{1/d}\).

1 citations1 influentialRead paper

Vector Coded Caching Multiplicatively Boosts MU-MIMO Systems Under Practical Considerations

Jan 20, 2026IEEE Transactions on Wireless Communications

This work addresses practical challenges in multi-user MIMO systems—such as path loss, channel state information (CSI) acquisition overhead, multi-antenna reception, and fairness constraints—by integrating vector-coded caching with multi-user MIMO. The authors propose a low-complexity block diagonalization–maximum ratio combining (BD-MRC) precoding scheme that combines zero-forcing (ZF), max-min fair power allocation, and a one-dimensional efficient search algorithm to enable cache-aided interference management. An asymptotic throughput expression is derived for Rayleigh fading channels under massive MIMO settings. Simulations demonstrate that, with a 32-transmit-antenna base station and two receive antennas per user, the proposed approach achieves over 300% throughput gain compared to a cache-free baseline, while maintaining robust performance under imperfect CSI.

1 citationsRead paper

The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization

Jan 17, 2026

This work addresses the challenge of voice anonymization by preserving linguistic content and emotional expression while concealing speaker identity. It introduces the first systematic evaluation framework that explicitly incorporates emotional fidelity as a core assessment dimension, establishing a multi-objective optimization paradigm that jointly optimizes privacy protection, semantic preservation, and emotional consistency. By integrating techniques such as speaker embedding perturbation, voice conversion, and generative modeling, and by introducing objective metrics based on adversarial attack models, the framework enables comprehensive evaluation of various baseline and submitted anonymization systems. Experimental results demonstrate that the proposed approach effectively balances privacy guarantees with speech utility, offering a new benchmark and guiding direction for future research in voice privacy.

1 citationsRead paper

Fundamental Limits of Multi-User Distributed Computing of Linearly Separable Functions

Jan 15, 2026

This study addresses the communication-computation trade-off in multi-user distributed computing for linearly separable functions. Under the coordination of a master node, N servers each compute at most M basis subfunctions and transmit linear combinations of their results to at most Δ users, with the goal of minimizing total communication cost. By jointly optimizing task allocation and linear encoding strategies, the work proposes an optimal scheme over the real field and rigorously establishes its optimality via an information-theoretic duality argument. In finite fields, it pioneers a combinatorial counting approach to derive fundamental performance bounds, fully characterizing the problem’s information-theoretic limits. The analysis systematically reveals the theoretical performance boundaries in both real and finite fields and achieves optimal communication cost across a wide range of parameter regimes.

1 citationsRead paper

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents

Jun 05, 2025

Traditional Expected Utility Theory (EUT) fails to capture human subjective perceptions—such as context-dependence and risk sensitivity—in goal-oriented semantic networks, leading to suboptimal resource allocation. Method: This paper pioneers the systematic integration of Cumulative Prospect Theory (CPT) into semantic resource allocation, modeling agents’ loss aversion, probability weighting distortion, and reference-point dependence. We formulate a non-expected utility optimization model grounded in CPT preferences and redesign multi-channel wireless power allocation policies accordingly. Results: In realistic semantic communication scenarios, our approach improves task completion rate by 32% and perceptual quality consistency by 27% over EUT-based baselines, significantly enhancing robustness against human cognitive biases. The core contribution lies in exposing EUT’s fundamental limitations in human-centered networks and establishing the first CPT-driven semantic resource allocation paradigm.

1 citationsRead paper
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