Most machine learning still follows a centralized model. Data is collected, sent to a server, and used to train large models in the cloud. But this approach runs into three problems at scale: privacy, bandwidth, and latency. Federated learning is emerging as an answer by moving training to the edge and keeping raw data local.
In federated learning, devices such as phones or IoT sensors train small updates to a shared model using their own data. Only the weight updates are transmitted back to a central server, where they are aggregated into a global model. This design ensures that sensitive data never leaves the device, reducing privacy risks while still contributing to collective intelligence.
The technical challenges are significant. Edge devices vary widely in compute power, network stability, and data distribution. Non-IID (independent and identically distributed) data makes convergence harder because each device sees a biased slice of the world. Communication efficiency is another hurdle: transmitting updates frequently can overwhelm networks, so methods like update compression and asynchronous aggregation are essential.
Security also requires attention. Malicious devices can attempt model poisoning by sending corrupted updates. Defenses include anomaly detection, Byzantine-resilient aggregation rules, and secure multi-party computation to protect the integrity of contributions.
Despite the challenges, federated learning is already in production. Google uses it for keyboard prediction on Android devices. Healthcare projects use it to analyze medical records across hospitals without centralizing sensitive data. Banks explore it to detect fraud collaboratively without exposing customer information.
Federated learning is more than a technique, it is a shift in how we think about machine intelligence. Instead of pulling all data into one place, intelligence is grown collectively across a network of devices. In a world where privacy and efficiency matter as much as accuracy, this distributed approach may define the future of AI.
References
https://arxiv.org/abs/1602.05629
Maurizio Morri - Science Blog 
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