Fetching the paper…
Reading the bibliography…
We present two novel federated learning (FL) schemes that mitigate the effect of straggling devices by introducing redundancy on the devices' data across the network.
C. Xie, S. Koyejo, and I. Gupta, “Asynchronous federated optimization,” Mar. 2019, arXiv:1903.03934
1903
Earlier work this paper cites.
1912
Earlier work this paper cites.
C. E. Shannon, “Communication theory of secrecy systems,” The Bell Syst. Tech. J. , vol. 28, no. 4, pp. 656–715, Oct. 1949
1949
Earlier work this paper cites.
A. Shamir, “How to share a secret,” Commun. ACM , vol. 22, no. 11, pp. 612–613, Nov. 1979
1979
Earlier work this paper cites.
2007
Earlier work this paper cites.
O. Catrina and A. Saxena, “Secure computation with fixed-point numbers,” in Proc. Int. Conf. Financial Crypto. Data Secur. (FC) , Tenerife, Spain, Jan. 2010, pp. 35–50
2010
Earlier work this paper cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur. (CCS) , Denver, CO, Oct. 2015, pp. 1322–1333
2015
Earlier work this paper cites.
J. Konec̆ný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” in NIPS Workshop Private Multi-Party Mach. Learn. (PMPML) , Barcelona, Spain, Dec. 2016
2016
Earlier work this paper cites.
S. Li, M. A. Maddah-Ali, and A. S. Avestimehr, “A unified coding framework for distributed computing with straggling servers,” in Proc. IEEE Globecom Workshops (GC Wkshps) , Washington, DC, Dec. 2016
2016
Earlier work this paper cites.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. Int. Conf. Artificial Intell. Stats. (AISTATS) , Fort Lauderdale, FL, Apr. 2017, pp. 1273–1282
2017
Earlier work this paper cites.
A. Jochems, “Developing and validating a survival prediction model for NSCLC patients through distributed learning across 3 countries,” Int. J. Radiat. Oncol. Biol. Phys. , vol. 99, no. 2, pp. 344–352, Oct. 2017
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur. (CCS) , Dallas, TX, Oct. 2017, pp. 1175–1191
2017
Earlier work this paper cites.
Q. Yu, M. A. Maddah-Ali, and A. S. Avestimehr, “Polynomial codes: an optimal design for high-dimensional coded matrix multiplication,” in Proc. Neural Inf. Process. Syst. (NIPS) , Long Beach, CA, Dec. 2017, pp. 4406–4416
2017
Earlier work this paper cites.
R. Tandon, Q. Lei, A. G. Dimakis, and N. Karampatziakis, “Gradient coding: Avoiding stragglers in distributed learning,” in Proc. Int. Conf. Mach. Learn. (ICML) , Sydney, Australia, Aug. 2017, pp. 3368–3376
2017
Earlier work this paper cites.
C. Karakus, Y. Sun, S. Diggavi, and W. Yin, “Straggler mitigation in distributed optimization through data encoding,” in Proc. Neural Inf. Process. Syst. (NIPS) , Long Beach, CA, Dec. 2017, pp. 5440–5448
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
K. Lee, M. Lam, R. Pedersani, D. Papailiopoulos, and K. Ramachandran, “Speeding up distributed machine learning using codes,” IEEE Trans. Inf. Theory , vol. 64, no. 3, pp. 1514–1529, Mar. 2018
2018
Cited alongside, same era.
K. Bonawitz et al. , “Towards federated learning at scale: System design,” in Proc. Mach. Learn. Syst. (MLSys) , Stanford, CA, Mar./Apr. 2019, pp. 374–388
2019
Cited alongside, same era.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization for heterogeneous networks,” in Proc. ICML Workshop Adaptive Multitask Learn. (AMTL) , Long Beach, CA, Jun. 2019
2019
Cited alongside, same era.
Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi, “Beyond inferring class representatives: User-level privacy leakage from federated learning,” in Proc. IEEE Int. Conf. Comp. Commun. (INFOCOM) , Paris, France, Sep. 2019, pp. 2512–2520
2019
Cited alongside, same era.
W. Wu, L. He, W. Lin, R. Mao, C. Maple, and S. Jarvis, “SAFA: A semi-asynchronous protocol for fast federated learning with low overhead,” IEEE Trans. Comput. , vol. 70, no. 5, pp. 655–668, May 2021
2021
Closest in time.
J. So, B. Güler, and A. S. Avestimehr, “Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,” IEEE J. Sel. Areas Inf. Theory , vol. 2, no. 1, pp. 479–489, Mar. 2021
2021
Closest in time.
Y. Zhao and H. Sun, “Information theoretic secure aggregation with user dropouts,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Melbourne, Australia, Jul. 2021, pp. 1124–1129
2021
Closest in time.
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Severinson, A. Graell i Amat, and E. Rosnes, “Block-diagonal and LT codes for distributed computing with straggling servers,” IEEE Trans. Commun. , vol. 67, no. 3, pp. 1739–1753, Mar. 2019
2019
Cited alongside, same era.
A. Reisizadeh, S. Prakash, R. Pedarsani, and A. S. Avestimehr, “Coded computation over heterogeneous clusters,” IEEE Trans. Inf. Theory , vol. 65, no. 7, pp. 4227–4242, Jul. 2019
2019
Cited alongside, same era.
S. Dutta, V. Cadambe, and P. Grover, “ “Short-Dot”
2019
Cited alongside, same era.
J. Zhang and O. Simeone, “On model coding for distributed inference and transmission in mobile edge computing systems,” IEEE Commun. Lett. , vol. 23, no. 6, pp. 1065–1068, Jun. 2019
2019
Cited alongside, same era.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag. , vol. 37, no. 3, pp. 50–60, May 2020
2020
Cited alongside, same era.
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimization,” in Proc. Neural Inf. Process. Syst. (NeurIPS) , Vancouver, Canada, Dec. 2020, pp. 7611–7623
2020
Cited alongside, same era.
S. Kadhe, N. Rajaraman, O. O. Koyluoglu, and K. Ramachandran, “FastSecAgg: Scalable secure aggregation for privacy-preserving federated learning,” in Proc. Int. Workshop Fed. Learn. User Privacy Data Confidentiality , Vienna, Austria, Jul. 2020
2020
Cited alongside, same era.
J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova, “Secure single-server aggregation with (poly)logarithmic overhead,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur. (CCS) , Nov. 2020, pp. 1253–1269
2020
Cited alongside, same era.
J. So, R. E. Ali, B. Güler, and A. S. Avestimehr, “Secure aggregation for buffered asynchronous federated learning,” in Proc. 1st NeurIPS Workshop New Frontiers Fed. Learn. (NFFL) , online, Dec. 2021
2021
Closest in time.
A. Frigård, S. Kumar, E. Rosnes, and A. Graell i Amat, “Low-latency distributed inference at the network edge using rateless codes,” in Proc. Int. Symp. Wireless Commun. Syst. (ISWCS) , Berlin, Germany, Sep. 2021
2021
Closest in time.
S. Prakash, S. Dhakal, M. R. Akdeniz, Y. Yona, S. Talwar, S. Avestimehr, and N. Himayat, “Coded computing for low-latency federated learning over wireless edge networks,” IEEE J. Sel. Areas Commun. , vol. 39, no. 1, pp. 233–250, Jan. 2021
2021
Closest in time.
S. Kumar, R. Schlegel, E. Rosnes, and A. Graell i Amat, “Coding for straggler mitigation in federated learning,” in Proc. IEEE Int. Conf. Commun. (ICC) , Seoul, South Korea, May 2022
2022
Closest in time.
R. Schlegel, S. Kumar, E. Rosnes, and A. Graell i Amat, “Straggler-resilient secure aggregation for federated learning,” in Proc. Eur. Signal Process. Conf. (EUSIPCO) , Belgrade, Serbia, Aug./Sep. 2022
2022
Closest in time.
A. R. Elkordy and A. S. Avestimehr, “HeteroSAg: Secure aggregation with heterogeneous quantization in federated learning,” IEEE Trans. Commun. , vol. 70, no. 4, pp. 2372–2386, Apr. 2022
2022
Closest in time.
J. So, C. He, C.-S. Yang, S. Li, Q. Yu, R. E. Ali, B. Güler, and S. Avestimehr, “LightSecAgg: a lightweight and versatile design for secure aggregation in federated learning,” in Proc. Mach. Learn. Syst. (MLSys) , Santa Clara, CA, Aug./Sep. 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
J. Nguyen, K. Malik, H. Zhan, A. Yousefpour, M. Rabbat, M. Malek, and D. Huba, “Federated learning with buffered asynchronous aggregation,” in Proc. Int. Conf. Artificial Intell. Stat. (AISTATS) , online, Mar. 2022
2022
Closest in time.
R. Schlegel, S. Kumar, E. Rosnes, and A. Graell i Amat, “Privacy-preserving coded mobile edge computing for low-latency distributed inference,” IEEE J. Sel. Areas Commun. , vol. 40, no. 3, pp. 788–799, Mar. 2022
2022
Closest in time.