diff --git a/federatedscope/core/trainers/torch_trainer.py b/federatedscope/core/trainers/torch_trainer.py
index 5c7e4c404..e3b36a242 100644
--- a/federatedscope/core/trainers/torch_trainer.py
+++ b/federatedscope/core/trainers/torch_trainer.py
@@ -289,6 +289,7 @@ def _hook_on_batch_end(self, ctx):
         ctx.y_true = None
         ctx.y_prob = None
 
+
     def _hook_on_fit_end(self, ctx):
         """Evaluate metrics.
 
@@ -302,6 +303,7 @@ def _hook_on_fit_end(self, ctx):
         results = self.metric_calculator.eval(ctx)
         setattr(ctx, 'eval_metrics', results)
 
+
     def save_model(self, path, cur_round=-1):
         assert self.ctx.model is not None
 
diff --git a/federatedscope/core/worker/client.py b/federatedscope/core/worker/client.py
index 715df910c..ba6b2743d 100644
--- a/federatedscope/core/worker/client.py
+++ b/federatedscope/core/worker/client.py
@@ -444,6 +444,15 @@ def callback_funcs_for_finish(self, message: Message):
             self.trainer.update(message.content,
                                 strict=self._cfg.federate.share_local_model)
 
+        # TODO: more elegant here
+        # Save final prediction result
+        if self._cfg.data.type == 'cikmcup':
+            # Evaluate
+            self.trainer.evaluate(target_data_split_name='test')
+            # Save results
+            self.trainer.save_prediction(self.ID, self._cfg.model.task)
+            logger.info(f"Client #{self.ID} finished saving prediction results.")
+
         self._monitor.finish_fl()
 
     def callback_funcs_for_converged(self, message: Message):
diff --git a/federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup.yaml b/federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup.yaml
index 957684f3d..f49950054 100644
--- a/federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup.yaml
+++ b/federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup.yaml
@@ -9,6 +9,7 @@ federate:
   make_global_eval: False
   total_round_num: 100
   share_local_model: False
+  client_num: 13
 data:
   root: data/
   type: cikmcup
diff --git a/federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup.yaml b/federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup.yaml
index 7880f4f3b..f4db08333 100644
--- a/federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup.yaml
+++ b/federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup.yaml
@@ -10,6 +10,7 @@ federate:
   make_global_eval: False
   total_round_num: 10
   share_local_model: False
+  client_num: 13
 data:
   batch_size: 64
   root: data/
diff --git a/federatedscope/gfl/dataset/cikm_cup.py b/federatedscope/gfl/dataset/cikm_cup.py
index 4240b2a07..3dfeb730d 100644
--- a/federatedscope/gfl/dataset/cikm_cup.py
+++ b/federatedscope/gfl/dataset/cikm_cup.py
@@ -7,7 +7,7 @@
 logger = logging.getLogger(__name__)
 
 class CIKMCUPDataset(InMemoryDataset):
-    name = 'CIKM_CUP'
+    name = 'CIKM22Competition'
 
     def __init__(self, root):
         super(CIKMCUPDataset, self).__init__(root)
diff --git a/federatedscope/gfl/trainer/graphtrainer.py b/federatedscope/gfl/trainer/graphtrainer.py
index 4b4f547ad..07c746a74 100644
--- a/federatedscope/gfl/trainer/graphtrainer.py
+++ b/federatedscope/gfl/trainer/graphtrainer.py
@@ -1,4 +1,7 @@
 import logging
+import os
+
+import numpy as np
 
 from federatedscope.core.monitors import Monitor
 from federatedscope.register import register_trainer
@@ -8,6 +11,10 @@
 
 
 class GraphMiniBatchTrainer(GeneralTorchTrainer):
+    def _hook_on_fit_start_init(self, ctx):
+        super()._hook_on_fit_start_init(ctx)
+        setattr(ctx, "{}_y_inds".format(ctx.cur_data_split), [])
+
     def _hook_on_batch_forward(self, ctx):
         batch = ctx.data_batch.to(ctx.device)
         pred = ctx.model(batch)
@@ -24,6 +31,14 @@ def _hook_on_batch_forward(self, ctx):
         ctx.y_true = label
         ctx.y_prob = pred
 
+        # record the index of the ${MODE} samples
+        if hasattr(ctx.data_batch, 'data_index'):
+            setattr(
+                ctx,
+                f'{ctx.cur_data_split}_y_inds',
+                ctx.get(f'{ctx.cur_data_split}_y_inds') + ctx.data_batch.data_index.detach().cpu().numpy().tolist()
+            )
+
     def _hook_on_batch_forward_flop_count(self, ctx):
         if not isinstance(self.ctx.monitor, Monitor):
             logger.warning(
@@ -69,6 +84,21 @@ def _hook_on_batch_forward_flop_count(self, ctx):
         self.ctx.monitor.total_flops += self.ctx.monitor.flops_per_sample * \
             ctx.batch_size
 
+    def save_prediction(self, client_id, task_type):
+        y_inds, y_probs = self.ctx.test_y_inds, self.ctx.test_y_prob
+        os.makedirs('prediction', exist_ok=True)
+
+        # TODO: more feasible, for now we hard code it for cikmcup
+        y_preds = np.argmax(y_probs, axis=-1) if 'classification' in task_type.lower() else y_probs
+
+        with open('prediction/prediction.csv', 'a') as file:
+            for y_ind, y_pred in zip(y_inds,  y_preds):
+                if 'classification' in task_type.lower():
+                    line = [client_id, y_ind] + [y_pred]
+                else:
+                    line = [client_id, y_ind] + list(y_pred)
+                file.write(','.join([str(_) for _ in line]) + '\n')
+
 
 def call_graph_level_trainer(trainer_type):
     if trainer_type == 'graphminibatch_trainer':