M. Abadi, A. Chu, I. J. Goodfellow, H. B. Mcmahan, I. Mironov et al., Deep Learning with Differential Privacy, 2016.

M. Alaggan, S. Gambs, and A. Kermarrec, , 2011.

, Private Similarity Computation in Distributed Systems: from Cryptography to Differential Privacy, OPODIS, pp.357-377

T. C. Aysal, M. E. Yildiz, A. D. Sarwate, and A. Scaglione, Broadcast Gossip Algorithms for Consensus, IEEE Transactions on Signal Processing, vol.57, issue.7, pp.2748-2761, 2009.

R. Bassily, A. D. Smith, and A. Thakurta, Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds, FOCS, 2014.

S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Randomized gossip algorithms, IEEE/ACM Transactions on Networking, vol.14, issue.SI, pp.2508-2530, 2006.

G. Cappon, G. Acciaroli, M. Vettoretti, A. Facchinetti, and G. Sparacino, Wearable continuous glucose monitoring sensors: A revolution in diabetes treatment, Electronics, vol.6, issue.3, 2017.

K. Chaudhuri, C. Monteleoni, and A. D. Sarwate, Differentially Private Empirical Risk Minimization, Journal of Machine Learning Research, vol.12, pp.1069-1109, 2011.

I. Colin, A. Bellet, J. Salmon, C. , and S. , Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions, ICML, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01329315

P. S. Dhillon, S. Sellamanickam, and S. K. Selvaraj, Semi-supervised multi-task learning of structured prediction models for web information extraction, CIKM, pp.957-966, 2011.

J. C. Duchi, M. I. Jordan, W. , and M. J. , Privacy Aware Learning, NIPS, 2012.

C. Dwork, Differential Privacy. In ICALP, 2006.

C. Dwork, F. Mcsherry, K. Nissim, and A. Smith, Calibrating Noise to Sensitivity in Private Data Analysis, TCC, 2006.

C. Dwork and A. Roth, The Algorithmic Foundations of Differential Privacy, Foundations and Trends in Theoretical Computer Science, vol.9, issue.3-4, pp.211-407, 2014.

C. Dwork, G. N. Rothblum, and S. Vadhan, Boosting and Differential Privacy, FOCS, 2010.

T. Evgeniou and M. Pontil, Regularized multi-task learning, KDD, 2004.

B. Goethals, S. Laur, H. Lipmaa, and T. Mielikäinen, On Private Scalar Product Computation for Privacy-Preserving Data Mining, ICISC, 2004.

J. Hamm, Y. Cao, and M. Belkin, Learning privately from multiparty data, ICML, 2016.

B. Hitaj, G. Ateniese, and F. Perez-cruz, Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning, CCS, 2017.

Z. Huang, S. Mitra, and N. Vaidya, Differentially Private Distributed Optimization. ICDCN, 2015.
DOI : 10.1145/2684464.2684480
URL : http://arxiv.org/pdf/1401.2596

P. Kairouz, S. Oh, and P. Viswanath, The Composition Theorem for Differential Privacy, ICML, 2015.

P. Kairouz, S. Oh, and P. Viswanath, Extremal Mechanisms for Local Differential Privacy, Journal of Machine Learning Research, vol.17, pp.1-51, 2016.

A. Kermarrec and F. Ta¨?anita¨?ani, Want to scale in centralized systems? Think P2P, Journal of Internet Services and Applications, vol.6, issue.1, p.12, 2015.
DOI : 10.1186/s13174-015-0029-1
URL : https://hal.archives-ouvertes.fr/hal-01199734

X. Lian, C. Zhang, H. Zhang, C. Hsieh, W. Zhang et al., Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent, NIPS, 2017.

A. Maurer, The Rademacher Complexity of Linear Transformation Classes, COLT, 2006.

A. Mcgregor, I. Mironov, T. Pitassi, O. Reingold, K. Talwar et al., The Limits of TwoParty Differential Privacy, FOCS, 2010.

A. Nedic, Asynchronous Broadcast-Based Convex Optimization Over a Network, IEEE Transactions on Automatic Control, vol.56, issue.6, pp.1337-1351, 2011.
DOI : 10.1109/tac.2010.2079650
URL : http://www.ifp.illinois.edu/%7Eangelia/broadcast_revised_submit2.pdf

A. Nedic and A. E. Ozdaglar, Distributed Subgradient Methods for Multi-Agent Optimization, IEEE Transactions on Automatic Control, vol.54, issue.1, pp.48-61, 2009.
DOI : 10.1109/tac.2008.2009515

M. A. Pathak, S. Rane, R. , and B. , Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers, NIPS, 2010.

A. Rajkumar and S. Agarwal, A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification, AISTATS, 2012.

S. S. Ram, A. Nedic, and V. V. Veeravalli, Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization, Journal of Optimization Theory and Applications, vol.147, issue.3, pp.516-545, 2010.

R. Shokri and V. Shmatikov, Privacy-Preserving Deep Learning, CCS, 2015.
DOI : 10.1109/allerton.2015.7447103

S. Song, K. Chaudhuri, and A. D. Sarwate, Stochastic gradient descent with differentially private updates, GlobalSIP, 2013.
DOI : 10.1109/globalsip.2013.6736861
URL : http://www.ece.rutgers.edu/%7Easarwate/pdfs/SongCS13sgd.pdf

P. Vanhaesebrouck, A. Bellet, and M. Tommasi, , 2017.

, Decentralized Collaborative Learning of Personalized Models over Networks, AISTATS

E. Wei and A. E. Ozdaglar, Distributed Alternating Direction Method of Multipliers, 2012.

E. Wei and A. E. Ozdaglar, On the O(1/k) Convergence of Asynchronous Distributed Alternating Direction Method of Multipliers, 2013.

S. J. Wright, Coordinate descent algorithms. Mathematical Programming, vol.151, pp.3-34, 2015.

Y. Zhou, D. M. Wilkinson, R. Schreiber, and R. Pan, Large-Scale Parallel Collaborative Filtering for the Netflix Prize, AAIM, 2008.