# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Federated Learning with Over-the-Air Computation  
One of the most difficult aspects of FL in wireless networks, known as federated edge learning (FEEL), is overcoming the communication bottleneck caused by the need for multiple devices to upload high-dimension model updates to the parameter server [3]. Various approaches were used to reduce the resulting latency, including removing slower devices,picking only those whose updates would greatly accelerate learning, and compressing the update data. Designing new, targeted multiple access systems for FEEL is another option. The main disadvantage of traditional orthogonal access techniques is that they do not adjust to the number of devices as well as they should. The radio resources required, in particular, expand linearly with the number of transmitters; otherwise, delay would increase linearly. The scalability required for multi-access in FEEL is provided by a developing approach known as over-the-air computation, also known as AirComp [3]. In fact, the aggregate of the models is achieved by utilizing the waveform's superposition property. As a result of the simultaneous access, the latency is unaffected by the number of devices. Given the devices' simultaneous and synchronized transmission, their signals are superimposed \"over-the-air\", and the PS receives their weighted sum, which is described as an aggregate signal; the weights correspond to the channel coefficients. It is preferable to have uniform weights in the case of AirComp FEEL, so that the aggregate signal is not oriented towards one device over another. Each device must modulate its own signal using analog linear modulation and invert the channel fading by regulating the transmission power in order for this to be achievable. The first procedure is required to exploit the analog waveforms' superposition property, and the second aligns the values that make up the distinct signals. The basic principle of AirComp is proposed in [3]. The use of seemingly basic analog modulations may raise questions about its utility; yet, it has been demonstrated that AirComp can be a great solution for minimizing the mean squared error (MSE) of the distortion when all channels and sources are Gaussian and independent. The time synchronization of device transmission is a critical need for the deployment of AirComp. Uplink transmissions (TDMA and SC-FDMA) for systems such as LTE and 5G also have this requirement. A fundamental synchronization process in these systems is known as \"timing advance\", and it can also be used to synchronize AirComp. This process requires involving each device in the estimation of the relevant propagation delay, and then transmitting this estimate ahead of time in order to eliminate the delay. When it comes to estimating the propagation delay, the precision of a synchronization channel is related to its bandwidth. With a bandwidth of 1 MHz, for example, the estimation error is less than 0.1 microseconds. When AirComp is used in an OFDM broadband system, the error causes just a phase shift in a symbol received on a sub-channel if the error is less than the cyclic prefix. This phase shift can then be adjusted. The cyclic prefix in an LTE system is several microseconds long, which is more than enough time to correct synchronization issues. Quantization and decoding mistakes cause distortion in digital modulations. The main sources of distortion in AirComp, on the other hand, are channel noise and interference, which directly disrupt the analogically modulated signals. In a broadband system, the spectrum is divided into sub-carriers, using OFDM modulation. The implementation of AirComp-FEEL in systems of this type involves the simultaneous and over-the-air aggregation of the model update coefficients, transmitted through the sub-carriers. AirComp FEEL was proposed in [3]. For each OFDM symbol, ideally each sub-carrier is linearly and analogically modulated, with a single element, whose power is determined by the channel inversion.