Recently we have all experienced a growing concern for data protection. It was connected with the fact that there has been an exponential growth in the number of intelligent devices worldwide, driven by the rise of the Internet of Things (IoT). A significant portion of these devices is equipped with multiple sensors and extremely powerful hardware, enabling them not just to gather data but, more significantly, to process it on a massive scale. Simultaneously, advancements in technology have revolutionized the methods for obtaining revolutionary access to data in various fields, including computational vision, language processing, speech recognition, and more. In this respect Federated Learning (FL) emerges as an important research area in response to strict data security regulations, making centralised data storage impractical. Let us explore the peculiarities of this ML learning strategy to understand whether Federated Learning can be an integral solution for data privacy issues. Federated Learning as a Concept Federated Learning, also known as collaborative learning, is a decentralized method for training machine learning models. It eliminates the need for transmitting data from client devices to global servers. Instead, the model is trained locally using raw data on edge devices, enhancing data privacy. The ultimate model is collaboratively created by consolidating the local updates. For a better understanding, let's take a look at the traditional machine learning training workflows. The typical machine learning (ML) training workflow involves a data scientist or ML engineer training a model on a centralized server or local machine, with the model present on the machine and the data potentially residing elsewhere. To optimize training, distributed training is introduced. Usually, when we are confronted with an immense undertaking in any professional sphere, the strategy involves breaking it down into smaller subtasks or steps and executing them simultaneously. This not only saves time but also makes such a complex task manageable. In deep learning, this approach is known as distributed training. In distributed training, the workload of training an extensive deep learning model is distributed across multiple processors, commonly known as worker nodes or simply workers. These workers undergo parallel training to expedite the overall training procedure. Essentially, there are two primary approaches to parallelism—data parallelism and model parallelism. Data Parallel Training Data parallel training involves slicing the data and distributing it across multiple worker nodes. Each worker has the same model but processes a different data slice. After a training cycle, each worker computes its vector of parameters, and these parameters are collated. The model on each worker is then updated with the new collated parameters. Model Parallel Training In model parallel training, the model is sliced, and each slice is deployed on a different worker node, while the data remains the same across all nodes. Unlike data parallel training, workers in model parallel training only synchronize shared parameters common to all model slices, making synchronization more efficient and outperforming data parallel training. Federated Learning Meanwhile, Federated Learning takes a different approach, conducting learning at the edge where the data is generated. Federated learning is similar to Data Parallelism, but its goal is different. While Data Parallelism (just like Model Parallelism) aims to optimize the training process (in other words, to eat an elephant a bite at a time), FL is called upon to preserve privacy. Thus, Data Parallelism involves dividing a large dataset that is kept in one place into parts that are sent to different working nodes, but FL denies the very need to consolidate data in one place. No matter how big an elephant is, it's already divided into pieces, and no one can see a piece of another or the entire elephant. Here is how it works — the initial model is sent to various edge nodes by a server, and each node trains its model with its generated data. From time to time, learned parameters are sent back to the server, which aggregates them to train a global model. This approach allows training to occur at the source of data generation, such as mobile devices or sensors, reducing the need for centralized data storage and promoting edge-based model training. In deciding between distributed and Federated Learning, various factors come into play. Privacy considerations favor Federated Learning, as it allows training at the edge, eliminating the need to send data to a server, and addressing concerns about data sharing with a server on a public cloud. Performance comparisons between model-parallel distributed training and Federated Learning depend on the network connectivity. For nodes connected over the internet, Federated Learning may outperform distributed training due to synchronization limitations in the latter. However, if nodes are on the same network, distributed training could be more efficient, leveraging powerful nodes and a fast network. In terms of cost, Federated Learning excels by reusing data-generating edge devices for both training and data generation, avoiding the need to purchase new hardware as required in distributed training. Types of Federated Learning Below we have listed the strategies that are most commonly used in Federated Learning. Centralized Federated Learning This approach relies on a central server for coordinating client device selection and collecting model updates during training, with communication occurring solely between the central server and individual edge devices. While centralized Federated Learning seems straightforward and produces accurate models, the central server poses a bottleneck problem, as network failures can impede the entire process. Decentralized Federated Learning On the other hand, decentralized Federated Learning eliminates the need for a central server to coordinate learning, as model updates are shared exclusively among interconnected edge devices. The final model is derived on an edge device by aggregating the local updates of connected devices, preventing the risk of a single-point failure. However, the model's accuracy is contingent on the network topology of the edge devices. Heterogeneous Federated Learning In heterogeneous Federated Learning, diverse clients, such as mobile phones, computers, or IoT (Internet of Things) devices, are involved. These devices may vary in terms of hardware, software, computation capabilities, and data types. HeteroFL addresses the limitations of common Federated Learning strategies that assume local models' attributes resemble those of the main model. In reality, this resemblance is rare. HeteroFL can produce a single global model for inference after training over multiple diverse local models. Benefits of Federated Learning As can be seen from the above, Federated Learning comes with several massive advantages and it is worth looking into them in more detail. Privacy Preservation One of the key benefits is its focus on safeguarding user privacy. By keeping data on local devices, it avoids sending raw information to a central server. Instead, only model updates are shared, ensuring that sensitive data stays secure and confidential. This decentralized approach is effective in addressing privacy concerns and complying with regulations. Data Security Federated Learning minimizes the need for extensive data transfers, reducing exposure to potential security vulnerabilities during transmission or storage. Since data remains on local devices, the risk of data breaches or unauthorized access is notably diminished. Efficient Use of Resources Clients contribute to model training using their own devices, eliminating the requirement for extensive data transfers and reducing computational and communication costs. By utilizing local computing resources, Federated Learning minimizes the dependence on massive centralized infrastructure. Collaborative Learning Federated Learning facilitates collective training of prediction models, especially on devices like mobile phones. This collaborative approach keeps training data on the device itself, fostering cooperation and knowledge sharing among devices while maintaining data privacy. Time Efficiency Organizations can efficiently address challenges in machine learning models through collaboration. For example, highly regulated sectors like hospitals can collaboratively train life-saving models while upholding patient privacy, accelerating the development process. Federated Learning eliminates the need for repeated efforts in collecting and aggregating data from diverse sources, saving valuable time. Improving Privacy There are 2 main aspects to classify privacy in Federated Learning: local and global privacy. The terms are self-explanatory —  local privacy deals with data at an individual level and involves sharing model updates instead of raw data, while global privacy ensures that the model updates remain private to any third parties, but for the central server. It's important to note that the updates, even in their current form, may be regarded as private data. To provide a mathematically rigorous level of confidentiality, it is necessary to combine Federated Learning (FL) with other Privacy-Enhancing Technologies (PeT). Such techniques as Differential Privacy Homomorphic Encryption, and Secure Multiparty Computation are applied to ensure the privacy of FD. Differential Privacy adds randomness, Homomorphic Encryption allows one to perform calculations on encrypted data, and Secure Multiparty Computation ensures devices collaborate without revealing private information. It is worth noting that although Federated Learning is an outstanding technology, revealing a lot of privacy concerns, it is not cryptography in its pure form. In other words, we're unable to prove the security of FL in a strictly mathematical way, so we cannot give any security guarantees that doesn't mean FL is not secure, but we simply cannot claim otherwise. Therefore, it makes sense to build solutions using the best of several technologies: FL and MPC, as an example. So we can implement MPC protocols to aggregate individual user updates so the central server won't see them, and the learning process can avoid model weight leakage. For more details, you can refer to the following . research on the secure multi-party computation for FL Use Cases of Federated Learning in Various Industries Federated Learning has already taken center stage in many industries. Thus, smartphones are making magic happen with word prediction, face recognition, and voice recognition in virtual assistants like Siri or Google Assistant. These applications improve user experiences by customizing interactions while still respecting privacy. Another example is transportation. Federated Learning is the brain behind the scenes, powering these vehicles with computer vision and machine learning. They analyze real-time surroundings and continuously learn from diverse datasets, making the learning process faster and the models more robust compared to traditional cloud-based approaches. Healthcare is yet another sphere which actively employs technology. The industry grapples with the challenge of scaling machine learning systems globally, hindered by the sensitive nature of healthcare data and stringent privacy constraints. Federated Learning here becomes a game-changer. It enables secure model training by accessing data from patients and medical institutions directly, all while keeping the data within its original premises. It facilitates collaborative efforts between individual institutions, empowering models to learn from diverse datasets securely. Beyond that, Federated Learning opens the door for clinicians to glean insights about patients or diseases from broader demographic areas, extending beyond local institutions. It also democratizes access to advanced AI technologies, offering smaller rural hospitals the capability to tap into the benefits of cutting-edge solutions. Limitations Alas, no technology is perfect and can become a one-fits-all solution. While considering the use of FL for any AI system, one should be aware of the limitations the approach can face. Communication Efficiency Woes Federated Learning hits a roadblock in the efficiency of communication, especially when dealing with a multitude of devices. Slow message transfers crop up due to factors like low bandwidth, limited resources, and the geographical spread of devices. To tackle this, strategies like local updating methods, model compression schemes, and the use of decentralized training in low bandwidth situations come into play. Privacy Dilemmas The privacy of user data is a big worry in Federated Learning. Even though data stays put on user devices, there is a risk of information leaks from shared model updates. To counter this, nifty privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation step in to save the day. Device Diversity The diversity in storage, communication, and computational capabilities across various devices in Federated Learning networks is a real challenge. Managing this diversity calls for tricks like asynchronous communication, active device sampling, and fault tolerance. Dealing with Data Differences Statistical heterogeneity throws a curveball, as data varies across client devices, challenging the assumption of identical and independently distributed (i.i.d) data. Variances in image resolution, language differences based on location, and other disparities can throw a wrench into data structuring, modeling, and inferencing in Federated Learning. Conclusion Federated Learning is a significant step forward in pursuing ethical and privacy-centric AI solutions despite its limitations. Across various sectors, including healthcare, transportation, and finance this groundbreaking approach is set to reshape how we manage sensitive data, turning the vision of secure, decentralized data processing into a tangible reality.

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Federated Learning Reimagined: Advancing Data Privacy in Distributed AI Systems

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Petr Emelianov

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