Federated Learning
Also known as: Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.
Key highlights
- Preserves data privacy by design: raw data never leaves the client device or institution.
- Scales to millions of heterogeneous edge devices without centralizing storage.
- Reduces data-transfer costs because only model updates, not datasets, are communicated.
- Enables learning from sensitive domains (healthcare, finance) where data sharing is legally restricted.
Intuition
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How it works
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When to use it
Use Federated Learning when training data is distributed across many clients and cannot be centralized due to privacy regulations, data-ownership constraints, or communication costs — for example, mobile keyboards, medical imaging across hospitals, or IoT sensor networks. Key assumptions include sufficient local data per client and a shared task across all participants. It is less suitable when client data distributions are highly heterogeneous (non-IID), communication bandwidth is severely limited, or when strong formal privacy guarantees beyond data locality are required (consider combining with differential privacy in that case).
Strengths & limitations
- Preserves data privacy by design: raw data never leaves the client device or institution.
- Scales to millions of heterogeneous edge devices without centralizing storage.
- Reduces data-transfer costs because only model updates, not datasets, are communicated.
- Enables learning from sensitive domains (healthcare, finance) where data sharing is legally restricted.
- Convergence can be slow and unstable when client data distributions are highly non-IID.
- Communication overhead across many rounds can be significant on low-bandwidth networks.
- Model updates can still leak information about local data through gradient inversion or membership inference attacks.
- Stragglers and dropped clients complicate synchronous aggregation and can bias the global model.
Common pitfalls
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Applications
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Frequently asked
Does Federated Learning guarantee complete privacy?
Not by itself. While raw data never leaves client devices, model updates (gradients) can still leak information through gradient inversion or membership inference attacks. Combining federated learning with differential privacy noise addition and secure aggregation protocols substantially strengthens privacy guarantees, but each layer adds its own accuracy or communication trade-offs.
How does Federated Learning handle non-IID data across clients?
Standard FedAvg can diverge when client data distributions differ sharply from one another — a phenomenon called client drift. Remedies include increasing local batch sizes, reducing local epochs, applying personalized federated learning variants (such as FedProx or SCAFFOLD), or using server-side momentum. The severity depends on the degree of statistical heterogeneity across clients.
How many clients and rounds are typically needed for convergence?
McMahan et al. showed that even with a small fraction of clients sampled per round (e.g., 10 out of 100), FedAvg converges within tens to hundreds of rounds for standard tasks. In practice the number depends on dataset size per client, degree of non-IID-ness, learning rate, and the number of local gradient steps performed before each aggregation.
Sources
- 1.McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
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Cite this page
ScholarGate. (2026, June 2). Federated Learning. ScholarGate. https://scholargate.app/privacy/federated-learning