called over-the-air computation (AirComp).These AirComp and can also be integrated with other updates compression based based work use uncoded analog transmission to send aligned acceleration techniques.Simulation results show that PhyArith client-side updates in the form of vector,each of which has further improves the communication efficiency by 1.5 to 3 times the same number of elements placed in the same order.Clients for training LeNet-5,compared with solutions only applying in these work are assumed to be equipped with a special updates compression.
Hvec ivi pro encode full#
Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks Tao Huang',Baoliu Yef,Zhihao Qu,Bin Tang',Lei Xief,Sanglu Lut National Key Laboratory for Novel Software Technology,Nanjing University,China College of Computer and Information,Hohai University,China Abstract-Federated learning is a very promising machine To improve the communication efficiency,methods based learning paradigm where a large number of clients cooperatively on reducing the size of updates needed to transmit have been train a global model using their respective local data.In this paper,we consider the application of federated learning in widely studied -,by performing techniques like quanti- wireless networks featuring uplink multiuser multiple-input and zation, addition to simply compressing multiple-output (MU-MIMO),and aim at optimizing the commu- client-side updates,there is also work focusing on improving nication efficiency during the aggregation of client-side updates the communication efficiency of federated learning by utiliz- by exploiting the inherent superposition of radio frequency ing the characteristics of networks.Specifically,for wireless (RF)signals.We propose a novel approach named Physical- Layer Arithmetic (PhyArith),where the clients encode their local networks where uplink multiuser multiple-input and multiple- updates into aligned digital sequences which are converted into output (MU-MIMO)is enabled,i.e.,clients can concurrently RF signals for sending to the server simultaneously,and the send their updates to the server in the same time-frequency server directly recovers the exact summation of these updates resource,recent work like -study exploiting the as required from the superimposed RF signal by employing a superposition of radio frequency (RF)signals to effectively customized sum-product algorithm.PhyArith is compatible with aggregate updates by averaging them,based on the technique commodity devices due to the use of full digital operation in both the client-side encoding and the server-side decoding processes.
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