Publications

Pre-prints

  1. L. Ballotta, N. Bastianello, R. M.G. Ferrari, K. H. Johansson. “Personalized and Resilient Distributed Learning Through Opinion Dynamics.” arXiv
  2. G. Carnevale, N. Bastianello. “Modular Distributed Nonconvex Learning with Error Feedback.” arXiv
  3. D. Deplano, N. Bastianello, M. Franceschelli, K. H. Johansson. “Optimization and Learning in Open Multi-Agent Systems.” arXiv
  4. X. Ren, N. Bastianello, K. H. Johansson, T. Parisini. “Communication-Efficient Stochastic Distributed Learning.” arXiv
  5. S. C. Anand, N. Bastianello. “Security of Distributed Gradient Descent Against Byzantine Agents.” arXiv
  6. M. Barreau, N. Bastianello, “Learning and Verifying Maximal Taylor-Neural Lyapunov functions.” arXiv
  7. C. Liu, N. Bastianello, W. Huo, Y. Shi, and K. H. Johansson, “A survey on secure decentralized optimization and learning.” arXiv
  8. N. Bastianello, C. Liu, K. H. Johansson. “Enhancing Privacy in Federated Learning through Local Training.” arXiv

Chapter

  1. N. Bastianello, L. Schenato, R. Carli. “Multi-Agent Optimization and Learning: A Non-Expansive Operators Perspective.” Encyclopedia of Systems and Control Engineering [to appear] doi, link, arXiv

Journal articles

  1. G. Carnevale, N. Bastianello, G. Notarstefano, R. Carli. “ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks.” IEEE Trans. Automatic Control [to appear] doi, link, arXiv
  2. S. M. Azimi-Abarghouyi, N. Bastianello, K. H. Johansson, and V. Fodor, “Hierarchical Federated ADMM.” IEEE Networking Letters, vol. 7, no. 1, pp. 11–15, Mar 2025 doi, link, arXiv
  3. U. Casti, N. Bastianello, R. Carli, S. Zampieri. “A Control Theoretical Approach to Online Constrained Optimization.” Automatica, vol. 176, p. 112107, 2025 doi, link, arXiv
  4. N. Bastianello*, D. Deplano*, M. Franceschelli, K. H. Johansson. “Robust Online Learning over Networks.” IEEE Trans. Automatic Control, vol. 70, no. 2, pp. 933 – 946, Feb 2025 [* equal contribution] doi, link, arXiv
  5. N. Bastianello, L. Madden, R. Carli, E. Dall’Anese. “A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors.” IEEE Trans. Automatic Control, vol. 69, no. 12, pp. 8722-8737, Dec. 2024 doi, link, arXiv
  6. N. Bastianello, R. Carli, S. Zampieri. “Internal Model-Based Online Optimization.” IEEE Trans. Automatic Control, vol. 69, no. 1, pp. 689-696, Jan. 2024 doi, link, arXiv
  7. N. Bastianello, R. Carli, A. Simonetto. “Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization.” Signal Processing, vol. 210, pp. 109089, Sep. 2023 doi, link, arXiv
  8. N. Bastianello, L. Schenato, R. Carli. “A novel bound on the convergence rate of ADMM for distributed optimization.” Automatica, vol. 142, pp. 110403, Aug. 2022 doi, link
  9. A. M. Ospina, N. Bastianello, E. Dall’Anese. “Feedback-Based Optimization with Sub-Weibull Gradient Errors and Intermittent Updates.” IEEE Control Systems Letters, vol. 6, pp. 2521-2526, 2022 doi, link, arXiv
  10. N. Bastianello, R. Carli, L. Schenato, M. Todescato. “Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence.” IEEE Trans. Automatic Control, vol. 66, no. 6, pp. 2620-2635, Jun. 2021 doi, link, arXiv
  11. N. Bastianello, A. Simonetto, R. Carli. “Prediction-Correction Splittings for Time-Varying Optimization with Intermittent Observations.” IEEE Control Systems Letters, vol. 4, no. 2, pp. 373-378, Apr. 2020 doi, link
  12. A. Olama, N. Bastianello, P. Da Costa Mendes, E. Camponogara. “Relaxed Hybrid Consensus ADMM for Distributed Convex Optimization with Coupling Constraints.” IET Control Theory & Applications, vol. 13, no. 17, pp. 2828-2837, Nov. 2019 doi, link

Conference proceedings

  1. G. Yang, X. Ren, N. Bastianello, T. Parisini. “State Estimation Using a Network of Observers: A Distributed Optimization Approach.” 2025 European Control Conference (ECC’25)
  2. D. Deplano, N. Bastianello, M. Franceschelli, K. H. Johansson. “Distributed Variance Consensus with Application to Personalized Learning.” IFAC Conference on Networked Systems (NecSys’25)
  3. A. Pasdar, C. Liu, N. Bastianello. “Robust Federated Learning with Multi-Step Aggregation.” IFAC Conference on Networked Systems (NecSys’25)
  4. X. Ren, N. Bastianello, K. H. Johansson, T. Parisini. “Distributed Learning by Local Training ADMM.” 2024 IEEE Conference on Decision and Control (CDC’24), Dec. 2024, pp. 7124-7129 doi, link
  5. N. Bastianello, A. I. Rikos, K. H. Johansson. “Asynchronous Distributed Learning with Quantized Finite-Time Coordination.” 2024 IEEE Conference on Decision and Control (CDC’24), Dec. 2024, pp. 6081-6088 doi, link, arXiv
  6. G. Carnevale, N. Bastianello, R. Carli, G. Notarstefano. “Distributed Newton Optimization with ADMM-Based Consensus.” Symposium on Systems Theory in Data and Optimization 2024
  7. A. Penacho Riveiros, Y. Xing, N. Bastianello, K. H. Johansson. “Real-Time Anomaly Detection and Categorization for Satellite Reaction Wheels.” 2024 European Control Conference (ECC’24), Jun. 2024, pp. 253-260 doi, link
  8. Ö. T. Demir, L. Méndez-Monsanto, N. Bastianello, E. Fitzgerald, G. Callebaut. “Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management.” 2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Jun. 2024, pp. 547-552 doi, link
  9. N. Bastianello, A. I. Rikos, K. H. Johansson. “Online Distributed Learning with Quantized Finite-Time Coordination.” 2023 IEEE Conference on Decision and Control (CDC’23), Dec. 2023, pp. 5026-5032 doi, link, arXiv
  10. D. Deplano*, N. Bastianello*, M. Franceschelli, K. H. Johansson. “A unified approach to solve the dynamic consensus on the average, maximum, and median values with linear convergence.” 2023 IEEE Conference on Decision and Control (CDC’23), Dec. 2023, pp. 6442-6448 [* equal contribution] doi, link
  11. G. Carnevale, N. Bastianello, R. Carli, G. Notarstefano. “Distributed Consensus Optimization via ADMM-Tracking Gradient.” 2023 IEEE Conference on Decision and Control (CDC’23), Dec. 2023, pp. 290-295 doi, link
  12. N. Bastianello, R. Carli. “ADMM for Dynamic Average Consensus over Imperfect Networks.” IFAC Conference on Networked Systems (NecSys’22), Jul. 2022, IFAC-PapersOnLine vol. 55, no. 13, pp. 228-233 doi, link
  13. N. Bastianello, A. Simonetto, E. Dall’Anese. “OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression.” Proceedings of The 4th Annual Learning for Dynamics and Control Conference (L4DC’22), PMLR 168, pp. 138-152 link, arXiv, code
  14. N. Bastianello. “tvopt: A Python Framework for Time-Varying Optimization.” 2021 IEEE Conference on Decision and Control (CDC’21), Dec. 2021, pp. 227-232 doi, link, arXiv, code
  15. N. Bastianello, E. Dall’Anese. “Distributed and Inexact Proximal Gradient Method for Online Convex Optimization.” 2021 European Control Conference (ECC’21), Jun. 2021, pp. 2432-2437 doi, link, arXiv
  16. N. Bastianello, A. Simonetto, R. Carli. “Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization.” 54th Asilomar Conference on Signals, Systems and Computers, Nov. 2020, pp. 47-52 doi, link, arXiv
  17. N. Bastianello, A. Simonetto, R. Carli. “Prediction-Correction Splittings for Nonsmooth Time-Varying Optimization.” 2019 European Control Conference (ECC’19), Jun. 2019, pp. 1963-1968 doi, link, arXiv
  18. N. Bastianello, A. Simonetto, R. Carli. “Prediction-Correction for Nonsmooth Time-Varying Optimization via Forward-Backward Envelopes.” 2019 International Conference on Acoustics, Speech, and Signal Processing (ICASSP’19), May 2019, pp. 5581-5585 doi, link, arXiv
  19. N. Bastianello, R. Carli, L. Schenato, M. Todescato. “A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks.” 2018 IEEE Conference on Decision and Control (CDC’18), Dec. 2018, pp. 3379-3384 doi, link, arXiv
  20. N. Bastianello, M. Todescato, R. Carli, L. Schenato. “Distributed Optimization over Lossy Networks via Relaxed Peaceman-Rachford Splitting: a Robust ADMM Approach.” 2018 European Control Conference (ECC’18), Jun. 2018, pp. 477-482 doi, link, arXiv

PhD Thesis

  • N. Bastianello, Supervisors: R. Carli, A. Simonetto. “Operator theory for optimization and learning.” University of Padova, 2021 pdf

Patent

  • A. Simonetto, N. Bastianello, R. Carli, T. T. Tchrakian. “Methods and Systems for Managing As-A-Service Systems in the Event of Connectivity Issues”, US-11886319-B2 link