# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Federated Learning with Over-the-Air Computation
However, due to power limits, reversing the sub-carriers is not feasible, so they are not transmitted. This is known as channel truncation. As all devices must have the same mapping between update coefficients and sub-carriers, channel truncation will remove the coefficients mapped to sub-carriers in deep fade because they cannot be remapped. Near-far problems can arise as a result of channel truncation, in which the fraction of deleted coefficients is substantially higher in devices close to the parameter server (PS) than in devices further away. This phenomenon causes biases and has a negative influence on learning. One option is to use channel truncation solely in circumstances of small-scale fading, which has two major benefits: equalization of the truncation ratio between devices and the ability for the PS to use data from extremely far away devices. SignSGD in a Broadband AirComp-FEEL System  
Let us look at a real-world example of how wireless channels effect AirComp-FEEL convergence. Consider the implementation of \(signSGD\) in an AirComp-FEEL broadband system. It requires the replacement of the linear analog modulation with a binary/BPSK modulation. Let us look at a real-world example of how wireless channels affect AirComp-FEEL convergence. Consider the application of \(signSGD\) in an AirComp-FEEL broadband system; it necessitates the substitution of a binary/BPSK modulation for the linear analog modulation. The PS choice is based on the received signal's sign, and corresponds to an _over-the-air majority vote_ approach that turns the received aggregate gradient into a binary vector. The averaged/aggregated gradient norm reaching during the cycles, represented with the notation \(\overline{G}\), is a standard yardstick for the model's convergence level, given a generic attenuation function. The expected value of \(\overline{G}\) for the considered system can be analyzed as a function of the given numbers of rounds and devices, which quantifies the speed of convergence. We have that:  
\[E[\overline{G}]\leq\frac{a}{\sqrt{N}}(f_{1}+\frac{1}{\sqrt{k}}f_{2}+b)\]  
in which the factors \(f_{1}\) and \(f_{2}/K\) correspond to the descent of the gradient. The two parameters a and b concern the effects of the wireless channel. In the ideal case with perfect channels, the values a = 1 and b = 0 are taken. If the channels are AWGN, they are expressed as:  
\[a_{AWGN}=\frac{1}{1-\frac{1}{K\sqrt{SNR}}},b_{AWGN}=\frac{f_{2}}{1-\frac{1}{K \sqrt{SNR}}}\]  
where SNR refers to the SNR of the device. They tend to the ideal channel when the factor \(K\sqrt{SNR}\) increases, because K removes noise through aggregation and raises SNR by increasing signal power. The convergence speed analysis can be used to estimate the number of communication rounds required to train the model. In the instance of FEEL, learning delay, measured in seconds, is a more practical option in terms of performance measures. AirComp achieves lower model accuracy than OFDMA, but it considerably reduces latency when there is a large number of devices. The experiment considered an AirComp-FEEL system with over 100 devices running on broadband channels. The purpose was to train a convolutional neural network for handwriting recognition using MNIST data. This has been also discussed in [65].