# Overview of Distributed Machine Learning Techniques for 6G Networks
## 2 Wireless Communication Technology  
Wireless communication technology has evolved through several generations. In the beginning, the original mobile network, also known as a 1G, supported voice transmission based on the frequency modulation technique in the 1980s. The next generation of wireless communication, known as 2G, supported digital signal communication over analog. It was supported by Global System Mobile Communications (GSM) and was able to send text and picture messages, along with voice-related services. Later in 1995, a 2.5G standard supporting voice and data communication was introduced. In 2001, a 3G standard was introduced with increased data transmission capabilities and various new video-related services, including video streaming, video conferences, and live video chat. In 2009, a 4G revolution occurred, supporting the high-quality video communication, HD video streaming, and online gaming-related services. 4G technology was succeeded by 5G in the last decade, supporting a plethora of new services and applications. 5G services can be classified into three groups, known as eMBB (enhanced mobile broadband services), URLLC (ultra-reliable low latency communications), and mMTC (massive machine type communication services) [11]. This service often has a stringent data rate, latency, reliability, and connection requirements. 5G technology has advanced with various new technology revolutions, such as the Internet of Things (IoT), MEC, mmWave, and MIMO technique. Additionally, various ML new ML-related services have also emerged for supporting more intelligent networking platforms. With evolving wireless technologies, various new transmission technologies have emerged and are used effectively. For example, code-division multiple access (CDMA) in 3G, MIMO, and orthogonal frequency-division multiplexing (OFDM) in 4G; the mmWave technique; and massive MIMO in 5G have improved data rates by several fold in every generation. The upcoming 6G network is expected to have a more flexible approach while utilizing the time-frequency and space resources to fulfill more goals. The upcoming 6G technology is expected to serve the 2030s' data-driven society with instant unlimited connectivity requirements [12]. The current version of 5G is insufficient to satisfy the requirements of newly developed forthcoming applications and services, and thus requires a major revolution. Therefore, 6G research already begun at the end of the last decade with the goal of providing improved data rates, trillion-bit-level connection capacity, and undetectable or nonexistent latency [2]. However, new challenges are emerging in the domain of 6G research and need to be handled carefully. Here we present the main 6G challenges, as shown in Figure 1:  
* **Device energy consumption:** With the emerging trends of IoT technology, it is expected that the 6G network will provide communication services for a large set of IoT devices with limited energy supply. Therefore, the 6G network will be required to support various energy-saving mechanisms for higher energy efficiency. Variousenergy harvesting techniques with energy-efficient communication and computation paradigms can help 6G to overcome the devices' energy scarceness. * **ML-related challenges:** Several new artificial intelligence applications will be integrated into the 6G network for creating an intelligent networking platform. However, this can also present several challenges, including higher resource requirements, additional energy costs, security, privacy aspects, etc. The edge-based distributed learning and the split learning technologies are expected to play key roles. * **Terahertz (THz) communication and corresponding challenges:** The upcoming 6G technology is expected to use the THz frequency band to fulfill the users' data requirements. However, higher propagation loss and limited communication ranges are some of the main challenges that need to be addressed.