Editing Federated Learning (section)

= 5.8 Open Challenges =

Federated Learning (FL) helps protect user data by training models directly on devices. While useful, FL faces many challenges that make it hard to use in real-world systems.<sup>[1]</sup><sup>[2]</sup><sup>[3]</sup><sup>[4]</sup><sup>[5]</sup><sup>[6]</sup>

* '''System heterogeneity''': Devices used in FL—like smartphones, sensors, or edge servers—have different speeds, memory, and battery life. Some may be too slow or lose power during training, causing delays. Solutions include using smaller models, pruning, partial updates, and early stopping.<sup>[1]</sup><sup>[6]</sup>

* '''Communication bottlenecks''': FL requires frequent communication between devices and a central server. Sending full model updates can overwhelm slow or unstable networks. To address this, update compression methods like quantization, sparsification, and knowledge distillation are used.<sup>[2]</sup><sup>[3]</sup>

* '''Statistical heterogeneity (Non-IID data)''': Each device collects different data based on user behavior and environment, leading to non-IID distributions. This can reduce global model accuracy. Solutions include clustered FL, personalized models, and meta-learning approaches.<sup>[2]</sup><sup>[5]</sup>

* '''Privacy and security''': Even if raw data stays on devices, model updates can leak sensitive information. Malicious clients may poison the model. Mitigation strategies include secure aggregation, differential privacy, and homomorphic encryption, though these may add computational and communication overhead.<sup>[2]</sup><sup>[3]</sup>

* '''Scalability''': FL must support thousands or millions of clients—many of which may drop out or go offline. Techniques like reliable client selection and hierarchical FL (with edge aggregators) improve scalability.<sup>[4]</sup>

* '''Incentive mechanisms''': Clients may not participate without benefits, especially if training drains battery or bandwidth. Solutions like blockchain-based tokens, credit systems, or reputation scores are being explored, but adoption remains low.<sup>[2]</sup>

* '''Lack of standardization and benchmarks''': FL lacks widely accepted benchmarks and datasets. Simulations often ignore real-world issues like device failure, non-stationarity, or network variability. Frameworks like LEAF, FedML, and Flower offer partial solutions, but more real-world testing is needed.<sup>[2]</sup><sup>[3]</sup>

* '''Concept drift and continual learning''': User data changes over time, which can make models outdated. Continual learning techniques like memory replay, adaptive learning rates, and early stopping help models stay relevant.<sup>[1]</sup><sup>[6]</sup>

* '''Deployment complexity''': Devices run different operating systems, have different hardware specs, and operate on varying network conditions. Managing model updates and ensuring consistent performance is difficult across such diverse platforms.

* '''Reliability and fault tolerance''': Devices may crash, lose connection, or send corrupted updates. FL systems must be resilient and able to recover from partial failures without degrading the global model.

* '''Monitoring and debugging''': In FL, training occurs across thousands of devices, making it hard to monitor model behavior or identify bugs. New tools are needed to improve observability and debugging in distributed systems.