def detect_poison_untrusted(self, **kwargs) -> dict[int, float]:
        """
        Detect poison given no trusted validation data

        :return: dictionary where keys are suspected poisonous device indices and values are performance differences
        """
        self.set_params(**kwargs)

        suspected = {}
        (
            train_data,
            valid_data,
            train_labels,
            valid_labels,
            train_prov,
            valid_prov,
        ) = train_test_split(self.x_train, self.y_train, self.p_train, test_size=self.pp_valid)

        train_segments = segment_by_class(train_data, train_prov, self.num_devices)
        valid_segments = segment_by_class(valid_data, valid_prov, self.num_devices)

        for device_idx, (train_segment, valid_segment) in enumerate(zip(train_segments, valid_segments)):
            filtered_data, filtered_labels = self.filter_input(train_data, train_labels, train_segment)

            unfiltered_model = deepcopy(self.classifier)
            filtered_model = deepcopy(self.classifier)

            unfiltered_model.fit(train_data, train_labels)
            filtered_model.fit(filtered_data, filtered_labels)

            valid_non_device_data, valid_non_device_labels = self.filter_input(valid_data, valid_labels, valid_segment)
            var_w = performance_diff(
                filtered_model,
                unfiltered_model,
                valid_non_device_data,
                valid_non_device_labels,
                perf_function=self.perf_func,
            )

            if self.eps < var_w:
                suspected[device_idx] = var_w
                train_data = filtered_data
                train_labels = filtered_labels
                valid_data = valid_non_device_data
                valid_labels = valid_non_device_labels

        return suspected

        Detect poison given no trusted validation data

        :return: dictionary where keys are suspected poisonous device indices and values are performance differences
        
art/defences/detector/poison/provenance_defense.py