Added pFedGraph to the Plato documentation.

Author: baochunli

docs/docs/examples/algorithms/8. Personalized Federated Learning Algorithms.md

@@ -107,6 +107,46 @@ Learning](https://arxiv.org/abs/2212.01197)," in Arxiv, 2022.
 
 ---
 
+### pFedGraph
+
+pFedGraph infers a collaboration graph on the server to compute personalized
+aggregation weights and then regularizes local training toward the aggregated
+model for each client.
+
+```bash
+cd examples/personalized_fl
+uv run pfedgraph/pfedgraph.py -c configs/pfedgraph_CIFAR10_resnet18.toml
+```
+
+**Reference:** Ye et al., "[Personalized Federated Learning with Inferred
+Collaboration Graphs](https://proceedings.mlr.press/v202/ye23b.html)," in Proc.
+International Conference on Machine Learning (ICML), 2023.
+
+!!! note "Alignment with the paper"
+    The pFedGraph aggregation strategy
+    (`plato/servers/strategies/aggregation/pfedgraph.py:13-324`) builds the
+    collaboration graph by computing cosine similarities over weight updates,
+    then projects each row onto the simplex to match the graph inference step
+    described in Section 3.2.
+
+    The server (`plato/servers/pfedgraph.py:17-71`) stores per-client aggregated
+    weights and returns them via `customize_server_payload`, mirroring the
+    client-specific aggregation in Algorithm 1.
+
+    The trainer wiring (`plato/trainers/pfedgraph.py:8-25`) combines
+    `PFedGraphUpdateStrategy` and `PFedGraphLossStrategyFromConfig`
+    (`plato/trainers/strategies/algorithms/pfedgraph_strategy.py:20-123`) to
+    capture the reference vector at round start and add the cosine similarity
+    regularizer from Equation 3.
+
+    This mirrors the official pFedGraph implementation's split between
+    server-side graph inference and client-side regularized optimization, while
+    Plato's defaults (cosine similarity + simplex-projected weights) provide a
+    concrete instantiation of the paper's model-similarity + dataset-size
+    formulation.
+
+---
+
 ### LG-FedAvg
 
 With LG-FedAvg only the global layers of a model are sent to the server for aggregation, while each client keeps local layers to itself.