Delay Minimization for Federated Learning Over Wireless Communication Networks

In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model parameters to a base station (BS) which aggregates the local FL models and broadcasts the aggregated FL model back to all the users. Since FL involves learning model exchanges between the users and the BS, both computation and communication latencies are determined by the required learning accuracy level, which affects the convergence rate of the FL algorithm. This joint learning and communication problem is formulated as a delay minimization problem, where it is proved that the objective function is a convex function of the learning accuracy. Then, a bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.

[1]  Kaibin Huang,et al.  Towards an Intelligent Edge: Wireless Communication Meets Machine Learning , 2018, ArXiv.

[2]  H. Vincent Poor,et al.  Performance Optimization of Federated Learning over Wireless Networks , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[3]  Zhi Ding,et al.  Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.

[4]  Mehdi Bennis,et al.  Wireless Network Intelligence at the Edge , 2018, Proceedings of the IEEE.

[5]  Kin K. Leung,et al.  When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[6]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[7]  Choong Seon Hong,et al.  Energy Efficient Federated Learning Over Wireless Communication Networks , 2019, IEEE Transactions on Wireless Communications.

[8]  Kaibin Huang,et al.  Broadband Analog Aggregation for Low-Latency Federated Edge Learning , 2018, IEEE Transactions on Wireless Communications.

[9]  Kin K. Leung,et al.  Energy-Efficient Radio Resource Allocation for Federated Edge Learning , 2019, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[10]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[11]  Geoffrey Ye Li,et al.  Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems , 2019, IEEE Journal of Selected Topics in Signal Processing.

[12]  Walid Saad,et al.  Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications , 2018, IEEE Transactions on Communications.

[13]  H. Vincent Poor,et al.  Energy-Efficient Wireless Communications With Distributed Reconfigurable Intelligent Surfaces , 2020, IEEE Transactions on Wireless Communications.

[14]  Krisztian Buza,et al.  Feedback Prediction for Blogs , 2012, GfKl.

[15]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[16]  Walid Saad,et al.  A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.

[17]  Joonhyuk Kang,et al.  Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data , 2019, 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[18]  Ronghong Mo,et al.  Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning , 2020, IEEE Journal on Selected Areas in Communications.

[19]  Geoffrey Ye Li,et al.  Reinforcement Learning Based Cooperative Coded Caching Under Dynamic Popularities in Ultra-Dense Networks , 2020, IEEE Transactions on Vehicular Technology.

[20]  Mihaela van der Schaar,et al.  Machine Learning in the Air , 2019, IEEE Journal on Selected Areas in Communications.

[21]  H. Vincent Poor,et al.  Scheduling Policies for Federated Learning in Wireless Networks , 2019, IEEE Transactions on Communications.

[22]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[23]  Walid Saad,et al.  Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks , 2018, IEEE Transactions on Wireless Communications.

[24]  Albert Y. Zomaya,et al.  Federated Learning over Wireless Networks: Optimization Model Design and Analysis , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[25]  Yong Wang,et al.  Low-Latency Broadband Analog Aggregation for Federated Edge Learning , 2018, ArXiv.

[26]  Peter Richtárik,et al.  Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.