Federated Learning: challenges and future developments

January 18, 2023
federative learning

Nowadays, an immense number of connected devices such as mobile devices, wearables, and connected vehicles generate a huge amount of data. Due to the fast-growing computational power of these devices, along with privacy concerns, there is an increasing need to store and process data locally – pushing the computation from the cloud to the edge. Thus, Artificial Intelligence (AI) is needed to leverage the value of Big Data.

Traditional Machine Learning approaches need to combine all data at one location, typically a cloud data center which may violate laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. One of the major examples is the European Union’s General Data Protection Regulation (GDPR).

Federated Learning enables devices to learn while keeping all the training data on the device. This Machine Learning technique eliminates the need to store all the data on one machine or in the cloud. Hence, Machine Learning algorithms, such as deep neural networks, are trained on multiple local datasets contained in local edge nodes. Instead of keeping the raw data in a centralized data center for training, Federated Learning leaves the raw data distributed on the client devices and trains a shared model on the server by aggregating locally computed updates.

Therefore, Federated Learning can mitigate many systemic privacy risks and costs resulting from traditional, centralized Machine Learning approaches.



Why do we need Federated Learning?

In simple words - what is meant to stay on your mobile device will remain on your mobile device. This ability allows anyone to enjoy using their smartphone or other devices while minimizing the risk of their personal data and information being leaked online. This is especially critical now that we live in an age where cyber-attacks are becoming more common as hackers and cyber-criminals are getting smarter and better at what they do.

Another reason why we need Federated Learning is that AI learning is implemented faster because the mobile device no longer needs to follow what the server tells it to do. This process is necessary for a mobile device to perform and act according to what it has learned through experience and by analyzing the individual data stored on the machine.  This training allows you to enjoy a faster-personalized experience with your smartphone as your mobile device will adapt to your lifestyle and needs without the need for it to wait on the central server. 



Federated Learning Applications


Digital Healthcare / Pharmaceuticals

Federated Learning has confidently found its way into digital health as patient privacy is paramount. In that sense, applications based on specific medical fields can employ collaborative efforts using training algorithms shared by other devices to a central server without uploading and divulging sensitive patient data and information. Pharmaceutical companies like AstraZeneca, Bayer, and Novartis have an operational Federated Learning predictive modeling platform for drug discovery. This Machine Learning process is still work in progress as medical practitioners and developers themselves are still exploring the possibility of using Federated Learning in medicine to improve the safety and privacy of patient data. 



Artificial Intelligence-Powered Voice Assistants

AI-Powered Voice Assistants like Google Assistant use Federated Learning with the voice recordings stored on users’ devices to help with modeling with their “Hey Google” detection. The Google Assistant learns how to adjust the modeling from the voice data it receives and then sends a condensed report of the data changes to Google’s servers. Google and other companies such as Apple, NVIDIA, and IBM collect data summaries from many of their users in this manner to improve their products all the time.



Automotive Industry

The Automotive Industry is using Federated Learning for its self-driving vehicles to combat, the constant menace of cybersecurity threats and harness the expansion of 5G and IoT edge devices into cars. To privately access the volumes of user data and not have to load it from self-driving vehicles like onto servers and keep it in place is absolutely priceless! Due to the modern software and hardware in a car, Federated Learning can be used for providing, storing, and processing data. Sensors enable the collection of data in real time.

Furthermore, complex graphics cards and computer systems enable data to be analyzed and used with the help of Machine Learning methods. Especially in the development and improvement of self-driving cars, the principle of Federated Learning comes into play. The cars are able to independently create Machine Learning models of traffic, pedestrians or accidents and thus, for example, maintain the necessary minimum distance at certain speeds or wait when turning until all pedestrians have crossed the road. In addition to collecting data, the car is thus able to make decisions on its own.

Federated Learning not only has the advantage of using information in a privacy-compliant manner, but also the advantage of processing data in real time: in a critical traffic situation, data can be processed directly by the car and there are no latencies due to transmission to and processing on central servers. In extreme cases, this can save lives, e.g., if a pedestrian absent-mindedly runs in front of a car. Finally, the trained models of the individual cars are consolidated at the manufacturer and used for the improvement and development of the cars.



Financial Industry

The financial industry relies heavily on AI for its capabilities, and the financial sector can become more accurate through Federated Learning. Besides, the use of technologies like Federated Learning and leaving the user’s information in place instead of storing in a single server environment shows much promise to these industries.



What are the biggest challenges?

Speed, sophistication, computing power, and storage are the four most significant challenges of Federated Learning. As Federated Learning is rather sophisticated, it requires equal sophistication in the tools it uses to streamline AI and ML. While solutions to these issues exist and are readily available, there are few data transactions. 


Federated Learning enables us to benefit from data to which access would not be otherwise possible. It is a very smart idea and implementation, especially in our divided world where regulation is only getting stronger and more separated.


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