Threats to Federated Learning: A Survey

Federated Learning and Hidden Privacy Threats

Federated learning lets devices train shared models while your data stays on your phone or laptop, not in a big server.
This sounds safer, but there are risks most people dont see.
Some bad actors can send wrong updates that change the model, or try to guess private info from the training process.
Researchers warn designers to think about these risks early, because small mistakes can leak real data.
In plain words, federated learning can protect data, yet it is not magic — and privacy can still be at stake.
Two main kinds of trouble are common: poisoning attacks where attackers corrupt the model, and inference attacks where secrets are guessed from model behavior.
Fixes exist but they often cost speed or accuracy, so trade offs are tough.
The goal now is building systems that are simple, fair and strong against attacks, improving robustness while keeping user data safe.
Learn about this so we demand better tech that actually keeps our data private.

Read article comprehensive review in Paperium.net:
Threats to Federated Learning: A Survey

🤖 This analysis and review was primarily generated and structured by an AI . The content is provided for informational and quick-review purposes.