[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-federated-learning::en":3,"gloss-cluster-federated-learning::en":23,"gloss-next-federated-learning::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"federated-learning","core-ai","Federated Learning","Federated learning trains a shared model across many devices or organizations without centralizing the raw data. Each participant trains locally on its own data, sends only model updates (gradients or weight deltas) to a coordinating server, and the server averages them — federated averaging — into an improved global model that is redistributed for the next round. Google pioneered it for Gboard's next-word prediction; hospitals and banks use the cross-silo variant to learn from data that regulation forbids pooling. It is not automatically private: gradients can leak training examples, so serious deployments layer on differential privacy and secure aggregation. Engineering challenges are real — devices drop out, data is wildly non-IID across participants, and communication is the bottleneck. For SaaS builders it matters as a compliance-friendly answer to \"train on customer data without ever holding it,\" particularly in healthcare, finance, and on-device products.","Federated learning trains one model across many devices or orgs by sharing weight updates, not raw data — key for privacy-bound ML.",null,[11,14,17,20],{"slug":12,"name":13},"differential-privacy","Differential Privacy",{"slug":15,"name":16},"fine-tuning","Fine-Tuning",{"slug":18,"name":19},"machine-learning","Machine Learning (ML)",{"slug":21,"name":22},"pii","Personally Identifiable Information (PII)",[24,28,32,36,39,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":58,"category":5,"name":59,"updated_at":35},"deep-learning","Deep Learning",{"slug":61,"category":5,"name":62,"updated_at":27},"diffusion-model","Diffusion Model"]