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| == Federated Learning in Edge Computing == | | == Overview and Motivation == |
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| === Overview ===
| | Federated Learning (FL) is a machine learning paradigm designed to train a shared global model using data distributed across a network of devices, without requiring the raw data to leave its original location. This decentralized approach is a transformative response to growing concerns about data privacy, ownership, regulatory compliance, and the inefficiencies of centralizing vast quantities of information in the cloud. |
| Federated Learning (FL) is a decentralized approach to training machine learning models. In FL, edge devices such as smartphones, sensors, or IoT gateways train models locally and only share model updates, not raw data. This minimizes privacy risks and reduces communication overhead. | |
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| When combined with Edge Computing (EC) which brings computational power closer to the data source—FL enables privacy-preserving, real-time intelligence across distributed systems. It is especially useful in domains like healthcare, smart cities, autonomous vehicles, and industrial IoT where data sensitivity and latency are key concerns.
| | Traditional centralized machine learning pipelines depend heavily on collecting data from edge devices (like mobile phones, smart sensors, or industrial machines) and transferring it to data centers for training. However, in the context of Edge Computing (EC)—a framework where data is processed close to the data source—this approach introduces major limitations. These include increased network congestion, higher energy consumption, latency-sensitive communication failures, and significant risks of privacy breaches or regulatory non-compliance (e.g., violations of GDPR or HIPAA). |
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| === Background ===
| | Federated Learning addresses these limitations by shifting the model training process to the data itself. Rather than transmitting sensitive data to the cloud, FL enables each device—known as a client—to train a model locally and send only model updates (like gradients or parameters) to a central server. This server then aggregates these updates into a global model and sends it back to the devices. Importantly, no raw data is ever shared. As a result, FL minimizes privacy risks and reduces the communication overhead typical of traditional systems. |
| In traditional machine learning, data is collected in a central server for training. This becomes inefficient and risky when devices generate massive volumes of private data at the edge. FL solves this problem by keeping data local.
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| A typical FL cycle involves these steps:
| | In edge computing contexts, where devices are highly distributed and may vary in computational power, connectivity, and data quality, FL has proven particularly beneficial. It allows real-time learning on-device, supports intermittent connectivity by enabling asynchronous updates, and reduces the need for central coordination in bandwidth-limited environments. |
| # The central server sends the global model to selected clients.
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| # Each client trains the model on its own local dataset.
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| # The client sends back updated model parameters to the server.
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| # The server aggregates the updates into a new global model.
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| # The process repeats for multiple rounds.
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| The global objective function is:
| | For example, Google's Gboard keyboard application uses FL to improve next-word prediction models. User typing data remains on-device while the model is trained locally; updates are periodically sent to Google's servers, aggregated, and redistributed. Similarly, Apple has employed FL for enhancing Siri and dictation features on iOS devices, all while ensuring personal voice data stays on the phone. |
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| F(w) = sum over k from 1 to N of [ λ_k * F_k(w) ]
| | In the healthcare sector, FL allows multiple hospitals to jointly train disease detection models (e.g., for chest X-rays or brain tumors) without exposing sensitive patient data. This enables improved accuracy through diverse data while preserving patient confidentiality. |
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| Where:
| | Ultimately, the motivation behind FL is to facilitate collaborative intelligence while preserving data locality, privacy, and low-latency inference. In an increasingly data-driven world, where trust, speed, and security are critical, Federated Learning is not just an optimization—but a necessity [1][2][3]. |
| - F_k(w) is the loss function on client k,
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| - λ_k = n_k / n,
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| - n_k is the number of samples on client k,
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| - n is the total number of samples across all clients.
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| This ensures clients with more data influence the model proportionally.
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| === Architectures ===
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| '''Centralized Federated Learning'''
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| In this structure, a central server manages model distribution and aggregation. Clients train locally and send updates to the server. This is easy to implement but creates a single point of failure.
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| '''Decentralized Federated Learning'''
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| There is no central server. Clients exchange model updates directly using peer-to-peer or blockchain protocols. While more resilient, this method is harder to synchronize and manage.
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| '''Hierarchical Federated Learning'''
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| Edge servers collect updates from nearby devices, aggregate them, and forward results to the cloud. This reduces latency and balances load between edge and cloud resources.
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| === Aggregation Algorithms ===
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| The most common aggregation method is Federated Averaging (FedAvg). Each client trains locally and sends updated weights, which the server averages. The formula is:
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| w_next = sum over k of [ λ_k * w_k ]
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| Where:
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| - w_k is the local model from client k,
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| - λ_k is based on the client’s data size.
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| When client data is highly variable (non-IID), FedAvg struggles. FedProx improves stability by adding a regularization term:
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| F_k(w) = Local loss + ρ * ||w_k - w_global||^2
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| Here, ρ is a tuning parameter and w_global is the last global model. This discourages clients from diverging too far from the shared model.
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| Local model updates also change to include the regularization in their gradient descent.
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| === Communication Efficiency ===
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| To reduce transmission costs and support weak network conditions, FL systems use:
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| - Gradient quantization: Sending compressed updates.
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| - Sparsification: Only sending most important updates.
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| - Local update batching: Performing multiple training steps before communicating.
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| - Client sampling: Selecting only a few clients each round.
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| These techniques save energy and bandwidth without significantly affecting model accuracy.
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| {| class="wikitable"
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| |+ Comparison: Federated Learning vs Traditional Machine Learning
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| ! Feature !! Federated Learning !! Traditional Machine Learning
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| | Data Location || On-device || Centralized
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| | Privacy Risk || Low || High
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| | Bandwidth Use || Low || High
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| | Latency || Low (local) || High (cloud round-trip)
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| | Scalability || High (with sampling and compression) || Moderate
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| |}
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| === Privacy and Security ===
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| Although FL avoids raw data collection, it is not completely immune to privacy threats. Attackers can reverse-engineer updates to infer sensitive information.
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| '''Differential Privacy'''
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| This technique adds random noise to updates to obscure individual data points. It satisfies the formula:
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| P(A(D) ∈ S) ≤ exp(ε) * P(A(D') ∈ S) + δ
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| Where D and D′ differ by one user's data, ε is the privacy budget, and δ is a small tolerance for failure.
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| '''Secure Aggregation'''
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| Uses cryptographic techniques so that the server only sees the sum of updates, not each one individually.
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| '''Homomorphic Encryption'''
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| Allows computations on encrypted updates. For example, with additive encryption:
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| Enc(a) + Enc(b) = Enc(a + b)
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| This keeps updates private even during aggregation.
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| === Applications ===
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| '''Healthcare'''
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| Hospitals collaboratively train disease diagnosis models without exchanging patient data. This protects privacy and supports regulation compliance.
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| '''Autonomous Vehicles'''
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| Cars learn from local driving environments and send encrypted updates. These updates help build a shared driving model without exposing personal or location data.
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| '''Smart Cities'''
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| FL supports distributed learning across traffic lights, pollution sensors, and public safety systems. The result is real-time learning with citizen privacy intact.
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| '''Mobile Applications'''
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| Apps like keyboard predictors and fitness trackers use FL to improve personalization while keeping your data local.
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| '''Industrial IoT'''
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| Factories and energy systems use FL to detect faults and optimize operations without exposing proprietary information.
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| === Challenges ===
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| FL faces several deployment issues:
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| '''Scalability'''
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| Large networks require efficient coordination, especially when devices are frequently offline or have variable resources.
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| '''Data Heterogeneity'''
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| Client data is often unbalanced and varies in quality. This affects model convergence and generalization.
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| '''Security Threats'''
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| Poisoning attacks, model backdoors, and inference threats require robust defenses like anomaly detection and trusted execution environments.
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| '''Incentives'''
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| Devices use battery and compute to participate in FL. Fair reward systems and contribution scoring are under active research.
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| '''Interoperability'''
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| FL systems must work across various device types, operating systems, and network conditions. Standard APIs and lightweight FL libraries are crucial.
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| === References ===
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| # Abreha, H.G., Hayajneh, M., & Serhani, M.A. (2022). Federated Learning in Edge Computing: A Systematic Survey. ''Sensors'', 22(2), 450.
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| # Lyu, L., Yu, H., & Yang, Q. (2020). Threats to Federated Learning: A Survey. ''arXiv preprint arXiv:2003.02133''.
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| # Li, T., Sahu, A.K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. ''IEEE Signal Processing Magazine'', 37(3), 50–60.
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| # Kairouz, P., et al. (2019). Advances and Open Problems in Federated Learning. ''arXiv preprint arXiv:1912.04977''.
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Overview and Motivation
Federated Learning (FL) is a machine learning paradigm designed to train a shared global model using data distributed across a network of devices, without requiring the raw data to leave its original location. This decentralized approach is a transformative response to growing concerns about data privacy, ownership, regulatory compliance, and the inefficiencies of centralizing vast quantities of information in the cloud.
Traditional centralized machine learning pipelines depend heavily on collecting data from edge devices (like mobile phones, smart sensors, or industrial machines) and transferring it to data centers for training. However, in the context of Edge Computing (EC)—a framework where data is processed close to the data source—this approach introduces major limitations. These include increased network congestion, higher energy consumption, latency-sensitive communication failures, and significant risks of privacy breaches or regulatory non-compliance (e.g., violations of GDPR or HIPAA).
Federated Learning addresses these limitations by shifting the model training process to the data itself. Rather than transmitting sensitive data to the cloud, FL enables each device—known as a client—to train a model locally and send only model updates (like gradients or parameters) to a central server. This server then aggregates these updates into a global model and sends it back to the devices. Importantly, no raw data is ever shared. As a result, FL minimizes privacy risks and reduces the communication overhead typical of traditional systems.
In edge computing contexts, where devices are highly distributed and may vary in computational power, connectivity, and data quality, FL has proven particularly beneficial. It allows real-time learning on-device, supports intermittent connectivity by enabling asynchronous updates, and reduces the need for central coordination in bandwidth-limited environments.
For example, Google's Gboard keyboard application uses FL to improve next-word prediction models. User typing data remains on-device while the model is trained locally; updates are periodically sent to Google's servers, aggregated, and redistributed. Similarly, Apple has employed FL for enhancing Siri and dictation features on iOS devices, all while ensuring personal voice data stays on the phone.
In the healthcare sector, FL allows multiple hospitals to jointly train disease detection models (e.g., for chest X-rays or brain tumors) without exposing sensitive patient data. This enables improved accuracy through diverse data while preserving patient confidentiality.
Ultimately, the motivation behind FL is to facilitate collaborative intelligence while preserving data locality, privacy, and low-latency inference. In an increasingly data-driven world, where trust, speed, and security are critical, Federated Learning is not just an optimization—but a necessity [1][2][3].