[2] Agarwal, M., Ganguly, B., Aggarwal, V.: Communication efficient parallel reinforcement learning. In: Uncertainty in Artificial Intelligence (2021)
[3] Al-Abbasi, A.O., Ghosh, A., Aggarwal, V.: DeepPool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 20(12), 4714–4727 (2019)
[4] Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning 3(1), 1-122 (2011)
[5] Chen, T., Giannakis, G.B.: Bandit convex optimization for scalable and dynamic IoT management. IEEE Internet of Things Journal 6(1), 1276-1286 (2018)
[6] Ding, D., Zhang, K., Basar, T., Jovanovic, M.: Natural policy gradient primal-dual method for constrained Markov decision processes. In: Advances in Neural Information Processing Systems (2020)
[7] Elgabli, A., Issaid, C.B., Bedi, A.S., Rajawat, K., Bennis, M., Aggarwal, V.: FedNew: A communication-efficient and privacy-preserving Newton-type method for federated learning. In: International Conference on Machine Learning (2022)
[8] Elgabli, A., Park, J., Bedi, A.S., Bennis, M., Aggarwal, V.: GADMM: Fast and communication efficient framework for distributed machine learning. Journal of Machine Learning Research 21(76), 1-39 (2020)
[9] Elgabli, A., Park, J., Bedi, A.S., Issaid, C.B., Bennis, M., Aggarwal, V.: Q-GADMM: Quantized group ADMM for communication efficient decentralized machine learning. IEEE Transactions on Communications 69(1), 164-181 (2020)
[10] Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Janoos, F., Rudolph, L., Madry, A.: Implementation matters in deep RL: A case study on PPO and TRPO. In: International Conference on Learning Representations (2020)
[11] Haliem, M., Mani, G., Aggarwal, V., Bhargava, B.: A distributed model-free ride-sharing approach for joint matching, pricing, and dispatching using deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 22(12), 7931-7942 (2021)
[12] Hosseinalipour, S., Brinton, C.G., Aggarwal, V., Dai, H., Chiang, M.: From federated to fog learning: Distributed machine learning over heterogeneous wireless networks. IEEE Communications Magazine 58(12), 41-47 (2020)
[13] Islamov, R., Qian, X., Richtarik, P.: Distributed second order methods with fast rates and compressed communication. In: Proceedings of the 38th International Conference on Machine Learning (2021)
[14] Jin, H., Peng, Y., Yang, W., Wang, S., Zhang, Z.: Federated reinforcement learning with environment heterogeneity. In: International Conference on Artificial Intelligence and Statistics (2022)
[15] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Dennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D'Oliveira, R.G.L., Eichner, H., Rouayheb, S.E., Evans, D., Gardner, J., Garrett, Z., Gascon, A., Ghazi, B., Gibbons, P.B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konecny, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Qi, H., Ramage, D., Raskar, R., Raykova, M., Song, D., Song, W., Stich, S.U., Sun, Z., Suresh, A.T., Tramér, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F.X., Yu, H., Zhao, S.: Advances and open problems in federated learning. Foundations and Trends® in Machine Learning 14(1-2), 1-210 (2021)
[16] Kakade, S.M.: A natural policy gradient. In: Advances in Neural Information Processing Systems (2001)