Federated Learning Vs Critical Data-Sensitive Environments

How to stay safe:
Educate the people about threats
Adapt processes to support resilience
Ensure adequate defences are in place

 

 

During the recent EIITF 2024, an annual event by VARINDIA, Dr. Arindam Sarkar, Asst. Professor, Dept. of Computer Science & Electronics, Ramakrishna Mission Vidyamandira, Howrah enlighted the gathering comprising of the esteemed technology leaders from the corporate world, CXOs, members of COMPASS, and Value-Added Resellers (VARs) from Eastern India. He began by discussing prominent topics like cyber fraud, generative AI, adversarial neural cryptography, and soft computing, then highlighted the current data security challenges in these areas. Gradually, he introduced the concept of machine learning, which led to an exploration of federated learning. VARINDIA takes this as an opportunity to explore the world of Federated Learning, its scope, its potential and possible challenges.

Data is as Precious as Gold
Today, Data is powerful and fuels decision-making and drives innovation across the globe. Businesses rely on data to understand customer behaviour, improve products, and stay competitive. As Dr. Sarkar from Ramakrishna Mission Vidyamandira wisely said, “We live in an age where data is as valuable as gold to businesses.” This truth extends beyond corporations to everyday users like us. My photographs, emails, and WhatsApp messages hold deep personal significance. Though all are precious, my photographs and videos carry an especially sensitive and cherished weight. Coming back to the business scenarios, data is now collected, analyzed, and protected for its immense value. Reemphasizing that treating data like gold, must be carefully safeguarded from theft, misuse, and breaches, emphasizing its worth and the responsibility that comes with handling it.

Suraksha Tootegi – Data Lootegi!
JAMTARA days are over, now we have two new budding but mischievous cyber attackers operating from Bharatpur (Rajasthan) and Mathura (UP). As per the recent study, almost 80% of cyber crimes are being done by them, shared Dr. Sarkar. Particularly, Aadhaar card fraud in India has emerged as a significant concern. Today, Aadhaar is a unique identity document for accessing government services, subsidies, and financial transactions. Fraudsters often exploit vulnerabilities in the system to commit identity theft, unauthorized use of Aadhaar numbers, or create fake Aadhaar cards. Common methods include phishing, SIM card cloning, and tampering with biometric data. The government has introduced security measures like two-factor authentication and biometric encryption, but vigilance and public awareness are crucial to preventing Aadhaar fraud.

Cyber criminals don’t wait, they are using all possible means to innovate and attack, such as Loan apps scams where extensive generative AI is being used for the attack. Similarly LLM tools are being used for the attack. Dr. Sarkar broadly divided them into three sub types:

The need of the hour is to upskill today for a safer tomorrow. Better tuning of machine learning models is essential for improving prediction accuracy. Fine-tuning involves adjusting hyper-parameters, selecting optimal features, and refining algorithms to better fit the data. By doing this, models can reduce errors, improve generalization to new data, and deliver more accurate predictions. In fields like healthcare, finance, and autonomous systems, precise model tuning is critical for reliable and high-quality outcomes.

ML is old, FL is emerging
Today machine learning (ML) is old, and federated learning (FL) is emerging. This is because people have fear of sharing their data on the cloud and have doubts about the security means deployed there. Thus, federated learning is a decentralized approach to machine learning where multiple devices or servers collaboratively train a model without sharing their raw data. Instead of centralizing data on a single server, each participant (like smartphones or edge devices) trains the model locally and only shares the model updates (like weights or gradients) with a central server.
So what is federated learning? In common words, federated learning is word prediction, face recognition for logging, or voice recognition while using Siri or Google Assistant are all examples of federated-learning-based solutions. It helps personalize the user experience while maintaining privacy.

As far as the definition is concerned, federated learning is a type of machine learning that focuses on decentralized model training, while machine learning is a branch of artificial intelligence (AI) that uses data and algorithms to mimic human learning.

The pros
Data prevention in federated learning is crucial for safeguarding sensitive information while enabling collaborative machine learning across decentralized devices or systems. Federated learning allows models to be trained on local data without transferring it to a central server, reducing the risk of data breaches and ensuring privacy.
It minimizes data transfer, which can save bandwidth and resources. Also, models can be adapted to specific user behaviors by training on local data. Federated learning works best in scenarios like mobile device applications, healthcare, and any situation where data privacy is critical.
How does federated learning, safeguard the data, which is of prime concern to the businesses:

Encryption: Data is encrypted during communication between devices and the central server to prevent interception.
Differential Privacy: Adding noise to the data ensures individual user information remains anonymous and untraceable.
Secure Aggregation: Only aggregated model updates are shared, preventing direct access to individual data points.

FL is still not fully immune
When it comes to risks associated, federated learning has its own bunch of challenges.

Security vulnerabilities: Federated learning can be vulnerable to security attacks, such as model poisoning, membership inference, and gradient inversion attacks. These attacks can affect model performance without being noticed.
Bias: Federated learning can be biased if the local data is not representative.
Communication costs: Federated learning can have high communication costs.
Data quality: The performance of federated learning depends on the quality of the data from local workers and the central server. If the data quality is low, the trained model may be ineffective or even harmful.

The future…
But these risks can be addressed too. Using robust encryption protocols, optimizing communication efficiency, developing efficient updating model techniques and enabling collaboration across multiple systems, networks, and organizations can to an extent minimise the risks and concerns.
So we can say that like every coin has two sides, federated learning also has its challenges and benefits. Still, federated learning is considered to be the future of machine learning because it's a privacy-preserving, efficient, and scalable approach. Federated learning is expected to redefine machine learning and have a significant impact across many industries. Till then Sit Tight, Stay Alert and Stay Safe!